&#34;Self-Regulating Device for Modulating Inflammation&#34;

ABSTRACT

A bioreactor is provided which contains cells capable of producing cytokine inhibitors in response to cytokines, in a manner regulated by the local or systemic milieu of an individual patient and predicted by mechanistic computational simulations. The bioreactor transfers the cytokine inhibitors to a patient in need of control of the inflammation process as part of a disease or condition in the patient, such as sepsis, trauma, traumatic brain injury, or wound healing. Related methods also are provided.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a divisional application of copending U.S. patentapplication Ser. No. 13/121,013, filed Mar. 31, 2011, which is aNational Stage of International Application No. PCT/US2009/058767, filedSep. 29, 2009, which in turn claims the benefit of U.S. ProvisionalPatent Application No. 61/100,845, filed Sep. 29, 2008, each of which isincorporated herein by reference in its entirety.

The Sequence Listing associated with this application is filed inelectronic format via EFS-Web and is hereby incorporated by referenceinto the specification in its entirety. The name of the text filecontaining the Sequence Listing is 144307_ST25.txt. The size of the textfile is 15,641 bytes, and the text file was created on Jun. 3, 2014.

A device is described for an extracorporeal bioreactor comprising cellsselected for their ability to produce cytokines and/or cytokineinhibitors for controlling inflammation in a patient.

One current goal of medicine is to facilitate the intrinsicself-renewing ability by relieving damaged tissues from their functionalburden and facilitating tissue healing. In order to achieve this goal,it is necessary to acknowledge, understand, and control acuteinflammation. We have developed mathematical models of inflammation thatinter-related inflammation and tissue damage/dysfunction. Ourtherapeutic goal is not to abolish inflammation per se but to reducedamage/dysfunction (i.e. promote healing) by modulating inflammation ina rational fashion based on these computational models.

To do so, a self-regulating device and related methods are describedherein for individualized regulation of inflammation. The basic conceptof the proposed device is to create negative feedback proportional tothe exact degree of inflammatory stimulus. More precisely, severalinflammatory mediators known as cytokines (protein hormones that induce,modulate, and augment inflammation) are in turn regulated by endogenousinhibitors. In the proposed device, the device would produce or releaseone or more units of the neutralizing protein for every one or more unitof a given inflammatory cytokine. The device would be a biohybriddevice, in which gene-modified cells are housed in a bioreactor ormatrix. The genetic modification(s) of the cells housed in this devicewould serve to 1) sense the levels of a given inflammatory cytokine orcytokines; 2) produce the appropriate levels of the appropriate cytokineinhibitor(s); and 3) possibly also release diagnostic markers that wouldserve to either delineate the degree of inflammatory cytokines producedby the patient, to delineate the degree of production of the cytokineinhibitor(s), or both (i.e. a “theranostic” device). These geneticmodifications could also include other diagnostic, cytotoxic, ortherapeutic proteins stimulated by the genetic elements that would sensethe presence of inflammatory cytokines in the patient. This device will,in theory, solve the current need for a personalized (yet standardized)inflammation-modulating therapy. The device will be standardized since,for a given disease, a single bioreactor or release matrix would beused. The device would be personalized since a given patient'sindividual production of cytokines (i.e. the quality and quantity ofoverall cytokine production) would be counteracted in a precise fashionand only as required and guided by the mathematical model of a giveninflammatory disease. Moreover, this device would both obviate the needfor a diagnostic method prior to determination of treatment of a giveninflammatory disease, since the device would essentially serve as adiagnostic through either the genetic elements that would sense thelevels of relevant inflammatory cytokines in the patient, or through theproduction of indicator proteins at levels proportional to theinflammatory cytokines produced by the patient. Finally, since thisdevice would greatly reduce the time from diagnosis (including possiblythe calibration of patient-specific computational simulations of aninflammatory disease) to treatment, since both diagnosis and treatmentwould be carried by the device in a simultaneous fashion. The reductionin time to treatment is especially important in inflammatory settingssuch as sepsis, trauma, traumatic brain injury, and wound healing, inwhich individual trajectories (e.g. length of stay in the hospital) andoutcomes (e.g. survival, death, or long-term scarring) may be determinedafter a short period of inflammation.

The device described herein could be used to modulate inflammation in,for example: 1) acute, systemic inflammatory diseases (e.g. sepsis,trauma); 2) chronic, systemic inflammatory diseases (e.g. rheumatoidarthritis); and 3) cancer with an inflammatory etiology. The devicecould also be used to reprogram inflammation in a local context (e.g. askin wound or psoriatic lesion) if interfaced appropriately with such awound or lesion. The device could be used to influence the early-, mid-,or latestage inflammatory response to any trauma (accidental oriatrogenic [e.g. surgical]) so as to bring about improved healing andrehabilitation. The device could serve solely as a therapeutic device,solely as a diagnostic device, or both. The device could be fine-tunedto each of these applications based on specifications for optimaloutcome given by mathematical and other computer simulations of theinterrelated inflammatory and damage/healing responses. This devicecould be used both in civilian and military settings. It could beapplied rapidly to start monitoring and/or modulating inflammation in apersonalized fashion, or could be applied at any point in time of theinflammatory response if its properties are tuned properly.

Current inflammation therapy can only be individualized to a smallextent, given that FDA approval requires that a device or drug beutilized in a standard fashion. This device would be 1) a standardizeddevice that would not require harvesting and culturing of a patient'scells, and yet 2) personalized, in that it would elaborate moleculesthat specifically neutralize only those inflammatory agents made by agiven patient, and at levels driven by the levels of these inflammatoryagents in each patient. This device would obviate the need for atime-consuming and expensive cycle of blood sampling, analysis ofinflammation biomarkers, calibration of patient-specific mathematicalmodels of inflammation, determination of individual-specific therapy,and implementation of that therapy. Instead, the device's generaloperating characteristics would be tuned to a given disease by ageneralized model, for example a mathematical model, of the disease(based on population data), and yet the specific degree of elaborationof molecules that antagonize each inflammatory agent would be driven bythe characteristics of a given patient. Thus, the device would offer thebenefit of personalized, rational inflammatory modulation that wouldtake much less time and resources to implement.

In its most general form, the device comprises a bioreactor seeded withcells, such as hepatocytes, genetically engineered to respond to acytokine with that cytokine's own inhibitor. An additional benefit isthat many different types of such genetically engineered cells could bemade and stored indefinitely in liquid nitrogen. The stored cells couldbe thawed and combined in the proportions prescribed by a computationalmodel of a given inflammatory disease, such as a mathematical modeldescribed by ordinary or partial differential equations or anagent-based model. Thus, another benefit of this device would be thetheoretical capability of a nearly infinite spectrum of operatingcharacteristics. Various embodiments of such a device could beconceived, including reservoirs or biomaterials sensitive to a givencytokine that release the inhibitor, implantation of cells geneticallymodified to express the inhibitor upon exposure to the cytokine, orbioreactors seeded with these genetically modified cells.

One exemplary procedure for creating and using the bioreactor version ofthis device is:

Utilize mathematical models of inflammation in a given systemicinflammatory disease to determine the optimal modulation of inflammationthat would result in reduced tissue damage/dysfunction. See, e.g.,United States Patent Publication Nos. 20030087285 and 20080228456 forexamples of modeling methods for, e.g., sepsis, wound healing, vocalfold damage, and, generally inflammation using both object-oriented(agent-based) and equation-based modeling. Currently, factors thatenhance damage/dysfunction in our existing mathematical models ofinflammation include circulating cytokines that activate macrophage,neutrophils, and TH1 cells (e.g. TNF, IL-6, IL-12, IFN-λ, IL-2) as wellas effector products such as nitric oxide, superoxide, andperoxynitrite. A computerized algorithm can search the parameter spaceof the mathematical model of acute inflammation, in order to determinewhat changes to the circulating cytokines characteristic of the inflamedstate (in which damage/dysfunction is high) will result in reducingdamage/dysfunction to levels characteristic of health.

Recombinant DNA constructs are made that consist of a promoter regionsensitive to a given cytokine and that cytokines endogenous inhibitor,based on the predictions of the mathematical model in Step 1. Examplesinclude: 1) tumor necrosis factor (TNF) and its endogenous inhibitor,soluble TNF receptor, 2) interleukin-1 (IL-1) and IL-1 receptorantagonist, 3) transforming growth factor-β1 (TGF-β1) and TGF-β1latency-associated peptide (LAP). Many additional examples also exist.The device could be tuned for more rapid or slower response to cytokinesby incorporating multiple copies of a given promoter element, or byusing promoter elements of various inherent sensitivities to cytokines.The device could be tuned for various degrees of suppression of a givencytokine by incorporating multiple copies of the gene for the cytokinesendogenous neutralizer.

Stable transfection in cells such as a hepatocyte cell line,hepatocytes, or other suitable cells, are made with the gene constructsdescribed in Step 2. This step may take place by first creating virusesthat contain the DNA constructs and subsequently infecting the cellsdescribed above, or by means of stable transfection methods. The celllines could be of human or non-human origin, although the likeliestembodiment would utilize human cells in order to reduce the likelihoodof immune reactions to nonhuman proteins.

The transfected cells are seeded into vessels that allow for nutrients,oxygen, etc. to be delivered to the cells in order to maintain theirviability. Bioreactors containing hepatocytes have been maintainedstably for a month or more.

The device is connected to a patient's circulation via, e.g., catheters.Alternatively, the device is connected in some fashion to a skin woundor other local site of inflammation.

Blood or another relevant bodily fluid from the patient is circulatedthrough the device. Inflammatory cytokines in the patient's bodily fluidwould stimulate the release of the cytokine-specific neutralizingproteins, with a rate and magnitude driven both by the device'scharacteristics and the patient's own characteristics of inflammation.With time, lower levels of the patient's inflammatory cytokines would bemade as the device's inflammation dampening process proceeds. As thishappens, less of the neutralizing proteins would be made, since thestimulus for their production would be lower. During this process, smallsamples of the inflow and outflow from the device could be removed foranalysis of cytokines and comparison to the predictions of themathematical model, to determine that the device is operating aspredicted and that this operation is predicted to result in reduceddamage. Alternatively, if the device is constructed so as to allow forthe direct or indirect detection of either the patient's inflammatorycytokines or of the inhibitor(s) produced by the bioreactor, thendiagnosis could be carried in this manner. Other blood parameters (e.g.liver transferases, bilirubin, etc.) could also be measured as adjunctmeasures of the function of the device and the patient's health status.Eventually, an inflammatory steady state compatible with improvedoutcome would be reached. The device may then be either disconnected orreplaced with another, similar device that modulates a related orsubsequent inflammatory process, or a device that modulates a knownco-morbidity or consequence of the inflammatory response (e.g. cancer).

As mentioned above, we have developed a series of mathematical models ofinflammation and its interactions with tissue damage and healing, withthe goal of understanding, predicting, and controlling inflammation(Kumar, R., et al., J. Theoretical Biol. 230, 145-155 (2004); Clermont,G. et al. Crit. Care Med. 32, 2061-2070 (2004); Chow, C. C. et al. Shock24, 74-84 (2005); Reynolds, A. et al. J. Theor. Biol. 242, 220-236(2006); Day, J. et al. J. Theor. Biol. 242, 237-256 (2006); Prince, J.M. et al. Mol. Med. 12, 88-96 (2006); Lagoa, C. E. et al. Shock 26,592-600 (2006); Constantine, G., et al. J. Pure Appl. Math.doi:10.1007/s10589-007-9118-9., (2007); and Upperman, J. S. et al. J.Pediatr. Surg. 42, 445-453 (2007); Li, N. Y. K., et al. PLoS ONE. 2008.3:e2789; and Tones, A. et al. Shock. 2009. 32:172-178). TheTranslational Systems Biology models we have developed to date (An, G.;et al. J. Burn Care Res. 2008. 29:277-2; Vodovotz, Y., et al. PLoSComput. Biol. 2008. 4:1-6; and Vodovotz, Y. et al. Math. Biosci.2009.217:1-10) have been based on multi-scale inter actions at thecell-tissue-organ-organism level and clinical trial simulations at thepopulation level, constructing both equation-based and agent-basedmodels of various degrees of granularity. These innovative modelsencompass the dynamics of relevant cells, cytokines, and the resultingglobal tissue dysfunction in order to begin to unravel theseinflammatory interactions. “Global tissue damage/dysfunction” isconceptually equivalent to “alarm/danger signals” released from stressedor necrotic cells, and serves as a proxy for the overall health of theorganism. Our published models describe and predict various features ofseptic shock (Redd, M. J., et al. Philos. Trans. R. Soc. Lond B Biol.Sci. 359, 777-784 (2004); Kumar, R., et al., J. Theoretical Biol. 230,145-155 (2004); Clermont, G. et al. Crit. Care Med. 32, 2061-2070(2004); Chow, C. C. et al. Shock 24, 74-84 (2005); and Constantine, G.,et al. J. Pure Appl. Math. doi:10.1007/s10589-007-9118-9., (2007)) andtrauma/hemorrhage (Kumar, R., et al., J. Theoretical Biol. 230, 145-155(2004); Reynolds, A. et al. J. Theor. Biol. 242, 220-236 (2006); andDay, J. et al. J. Theor. Biol. 242, 237-256 (2006)), including thesimulation of anti-inflammatory strategies in clinical trials (Clermont,G., et al. Crit. Care Med. 2004. 32:2061-2070; Mi, Q. et al. Wound Rep.Reg. 2007. 15:671-682; Kumar, R. et al. Shock. 2008. 29:104111.and An,G., et al. J. Crit. Care 22, 169-175 (2007)).

The basic concept of the proposed device is to create negative feedbackproportional to the exact degree of inflammatory stimulus. Moreprecisely, for every unit of a given inflammatory cytokine, the devicewould produce or release essentially one unit of the neutralizingprotein. Examples include: 1) tumor necrosis factor (TNF) and itsendogenous inhibitor, soluble TNF receptor (An, G. et al. J. Burn CareRes. 29, 277-285 (2008); Gallucci, S. et al. Curr. Opin. Immunol. 13,114-119 (2001); Vodovotz, Y. Immunologic Res. 36, 237-246 (2006); andAggarwal, B. B. et al. Ernst. Schering. Res. Found. Workshop 161-186(2006)); and, 2) interleukin-1 (IL-1) and IL-1 receptor antagonist(Hasegawa, A., et al. Takasaki, W., et al. Mini. Rev. Med. Chem. 1, 5-16(2001)), 3) transforming growth factor-β1 (TGF-β1) and TGF-β1latency-associated peptide (LAP, Femandez-Botran, R., et al. Expert.Opin. Biol. Ther. 2, 585-605 (2002) and Böttinger, EP., et al., Proc.Natl. Acad. Sci. USA Vol. 93, pp. 5877-5882, June 1996). Many additionalexamples also exist. Various embodiments of such a bioreactor devicecould be conceived, including reservoirs or biomaterials sensitive to agiven cytokine that release the inhibitor, implantation of cellsgenetically modified to express the inhibitor upon exposure to thecytokine, or bioreactors seeded with these genetically modified cells(FIG. 1). The proof of concept studies described here would take thelatter approach.

Importantly, there must be a rational process by which to tailor thespecific characteristics of such a device. For example, the specificcytokines to be antagonized and the timing and magnitude of suchmanipulation will vary depending on the nature of the inflammatorydisease targeted. The mathematical models we have created are thereforeuseful for the rational construction and utilization of this device.

A bioreactor is therefore provided, for example, comprising acompartment comprising cells comprising a chimeric gene. The chimericgene comprises a response element operably linked to a sequence encodinga cytokine or an inhibitor of a cytokine, in which the response elementcauses expression of the cytokine or causes expression of the inhibitorof the cytokine when the cells are contacted with the cytokine. Thebioreactor comprising a selectively permeable membrane in contact withthe cells. As disclosed herein, the gene can express a cytokineinhibitor of one of TNF, IL-1, TGFβ1 and IL-6, such as TNF receptor,IL-1 receptor agonist, TGF-β1 LAP (latency-associated peptide) and anIL-6Ralpha/gp130 fusion protein.

The selectively permeable membrane can be a selectively-permeable hollowfiber. Alternately, the compartment comprising the cells can comprise avessel having a selectively permeable wall. The vessel may comprise aplurality of selectively permeable hollow fibers passing through thecompartment through which one or both of a gas and a fluid comprisingnutrients for the cells can be passed. In another embodiment, thecompartment comprising the cells comprises a plurality of selectivelypermeable hollow fibers passing through the compartment in which theplurality of hollow fibers are fluidly connected to a plasma or bloodcirculation system in which blood or plasma from the patient can becirculated through the hollow fibers and into a patient.

The cells may be any cell that is effective in its use in thebioreactor, and may be xenogeneic, syngeneic, allogeneic, or autologouscells to a patient treated by use of the bioreactor. In one embodiment,the cells are transfected or transduced hepatocytes or a hepatocyte cellline, such as HepG2. The bioreactor may further comprise a cellcomprising a (nucleotide) sequence encoding a fluorescent protein thateither is: a) operably linked to the response element and the sequenceencoding the cytokine or inhibitor of the cytokine is attached to and inframe with the sequence encoding the fluorescent protein and aself-cleaving polypeptide sequence between the sequence encoding thecytokine or inhibitor of the cytokine and the sequence encoding thefluorescent protein; or b) under control of a second response element(in the cell or a second cell) that causes expression of the fluorescentprotein when the cells are contacted with the cytokine. Alternately, oneor more of the cytokines or inhibitors of cytokines encoded by the oneor more non-native inducible genes comprises a fluorescent tag that iscontiguous with the one or more of the cytokines or inhibitors ofcytokines.

Also described herein is a method of modulating (controlling, affecting)wound healing, sepsis, trauma, or traumatic brain injury (TBI),comprising, contacting a bodily fluid of a patient with the selectivelypermeable membrane of the bioreactor of claim 1 such that a cytokine inthe bodily fluid can pass through the selectively permeable membrane anda cytokine or cytokine inhibitor produced by the cells can pass into thebodily fluid, and returning the bodily fluid to the patient. Thebioreactor may be any bioreactor described herein. The cells, theirquantity and chimeric genes they express, may be selected by use of acomputer model of an inflammatory response characteristic of a diseaseor condition in the patient. The method may include modelinginflammation associated with wound healing, sepsis, trauma or TBI anddetermining one or more cytokines to inhibit or produce to controlinflammation in the patient associated with sepsis, wound healing ortrauma. In one embodiment, data obtained from a patient may be used toassist in modeling inflammation or in tailoring the treatment to apatient. For example, the method may comprise determining levels of oneor more cytokines in the patient and modeling inflammation using the oneor more levels of cytokines in the patient and determining a cytokinelevel to be controlled in the patient to determine a chimeric geneconstruct to place in the bioreactor based on an outcome of themodeling. The method may comprise determining levels of one or morecytokines in the patient and modeling inflammation using the one or morelevels of cytokines in the patient and determining a cytokine level tobe controlled in the patient to determine a chimeric gene construct toplace in the bioreactor based on an outcome of the modeling. In oneembodiment, the patient is a TBI patient, and for example, one or bothof an inhibitor of TNF and an inhibitor of IL-6 are produced by thecells. In another embodiment, the cells comprise one or more genes thatexpress an inhibitor of one or both of TNF and IL-1α or IL-1β. Inanother embodiment, the gene expresses an inhibitor of a cytokineselected from the group consisting of soluble TNF receptor, IL-1receptor agonist, and TGF-β1 LAP (latency-associated peptide).

In further embodiments, the compartment comprising the cells andcomprises a plurality of selectively permeable hollow fibers passingthrough the compartment in which the plurality of hollow fibers arefluidly connected to a plasma or blood circulation system in which bloodor plasma from the patient is circulated through the hollow fibers andinto the patient. In another embodiment, the compartment comprising thecells has at least one wall that is the selectively permeable membrane,in which the first side of the membrane is placed in contact with awound on the patient or a bodily fluid in situ in the patient. In thatembodiment, optionally, the compartment comprises a plurality ofselectively permeable hollow fibers passing through the compartmentthrough which one or both of a gas and a fluid comprising nutrients forthe cells is passed.

FIG. 1 is a depiction of a self-regulating, individualized theranosticdevice for the detection and adaptive modulation of inflammation and onepossible method of connection of the device to a patient.

FIG. 2 is a schematic diagram showing a simplified embodiment of ahollow-fiber bioreactor device.

FIG. 3 is a schematic diagram of a “transdermal-type” bioreactor deviceas described herein.

FIG. 4A is a schematic diagram depicting a transdermal-type bioreactordevice comprising hollow fibers, as described herein. FIGS. 4B and 4Care cross-sections of two exemplary embodiments of the device of FIG.4A, along line A. FIG. 4D. Temporary artificial flat sheet membrane“bag” placed on a wound and perfused with medium. Hollow fibers withinthe bag are not shown.

FIG. 5. Iterative process of modeling and experimentation. Initial modelcomponents are determined from experimental data using PrincipalComponent Analysis. Subsequently, model building follows an iterativeprocess involving calibration from existing or new data, and validationfrom prediction of data. This process identifies both areas where amodel is correct and where it is deficient relative to data andtherefore must be corrected.

FIGS. 6A and 6B. sTNFR and IL-1ra plasmid. The NF-κB-responsive elementand sTNFR gene were obtained as described in the text (shownschematically in FIG. 6A). FIG. 6B shows the results of sequencing of aportion of the plasmid comprising the IL-1ra expressing gene (SEQ ID NO:1).

FIGS. 7A and 7B. Initial proof-of-concept experiments. FIG. 7A: HepG2cells were cultured in standard 2D tissue culture and transfected withnegative control plasmids (pcDNA3; negative control), a construct inwhich the constitutively active CMV promoter drives the expression ofsTNFR (msTNFR1; positive control), or the TNF-driven sTNFR promoter(sTNFR1-pcDNA3). In each case, the cells were either unstimulated orstimulated with 10 ng/mL mouse TNF. The graph shows the levels of sTNFRproduced under each of these conditions.

FIG. 8. Dose-dependent activation of sTNFR by TNF in transfected HepG2cells. HepG2 cells were treated as in FIG. 7, except that escalatingdoses of mouse TNF were used as a stimulus. All assessments of sTNFRwere made 24 h following stimulation with TNF.

FIG. 9-HepG2 cells transfected with this plasmid were placed in abioreactor and tested for their response to TNF. Following 3 days ofculture (baseline), the cells were stimulated with 3 ng/mL mouse TNF. 24h later, as well as a further 24 h after washing out the bioreactor withculture medium, mouse sTNFR was assayed as in FIG. 8.

FIG. 10. Characterization of HepG2 cells in 2-D and 3-D culture. HepG2cells were cultured in standard 2-D tissue culture (Panel A) as well asin experimental-scale liver bioreactors (Panel B); note the similarappearance of the cells. Panel C shows that HepG2 cells can survive andremain metabolically active for up to 100 days in bioreactor culture,producing glucose (red line) and lactate (blue line).

FIG. 11. Detection of fluorescence in transfected HepG2 cells in 2-D andbioreactor cultures. Panel A and B: HepG2 cells were transfected withpTurboFP635N, in which the fluorescent protein is under control of theCMV promoter. Panel A: bright field microscopy. Panel B: fluorescencemicroscopy. Panel C: Primary human fetal liver cells (˜18 weeksgestation) were cultured for 10 days in a 4-well bioreactor. Tissue thathad formed was fixed with 4% paraformaldehyde, embedded in paraffin, andsections were stained with DAPI (blue fluorescence) for cell nuclei andfor α-fetoprotein (green fluorescence).

FIG. 12. Expression and modeling of TNF and sTNFR in endotoxemic mice.Studies were carried out on C57B1/6 mice injected intraperitoneally with3 mg/kg LPS for the indicated time points. TNF (symbols; Panel A) andsTNFR (symbols; Panel B) were measured in the serum using specificELISA's. A mathematical model was fit to the data in Panel A. We proposeto do the same with data similar to those in Panel B.

FIG. 13. Endotoxin- and E. coli-induced TNF production and bacterialdynamics in rats. Panel A: Rats (n=4 per time point) were injectedintraperitoneally with 3 mg/k LPS. The rats were euthanized at theindicated time points and TNF was assayed using a rat-specific ELISA(R&D Systems, Minneapolis, Minn.). Values are mean±SD. The linerepresents the output of a mathematical model calibrated on these dataas well as data on IL-6, IL-10, and NO reaction products (NO₂ ⁻/NO₃ ⁻)at this dose of LPS as well as additional stimuli (6 and 12 mg/kg LPS,surgical cannulation trauma, and surgical cannulation+hemorrhagic shock(30 mmHg for multiple time points). Panel B: Rats (n=3-5 per time point)were subjected to surgical implantation of a fibrin clot containingapproximately 1.5×10⁸ E. coli bacteria (see Ref. 21 for protocol). Inthis experimental model, approximately 25% of the rats died in the 96-hperiod of observation. Surviving rats were euthanized at the indicatedtime points and TNF was assayed as in Panel A. Panel C: peritonealbacterial counts in the rats of Panel B.

FIG. 14A shows a plasmid map for plasmidpLenti6-3×NFkB-sTNFR-Ires-TurboFP. FIG. 14B shows a partial sequence ofthe plasmid depicted in FIG. 14A (SEQ ID NO: 2) identifying pertinentelements in that sequence.

FIG. 15 is a graph showing response of two NF-kB responsive promoters.

FIG. 16A shows a plasmid map for plasmidIL1RE-IL1ra-IresTurboFP-lenti6.3. FIG. 16B shows apartial sequence ofthe plasmid depicted in FIG. 16A (SEQ ID NO: 3) identifying pertinentelements in that sequence.

FIG. 17A shows a plasmid map for plasmidpLenti6-3×NFkB-sTNFR-T2A-TurboFP. FIG. 17B shows a partial sequence ofthe plasmid depicted in FIG. 17A (SEQ ID NO: 4) identifying pertinentelements in that sequence.

FIG. 18A shows a plasmid map for plasmid pLENTI6-3×NFkB-TurboFP. FIG.18B shows a partial sequence of the plasmid depicted in FIG. 18A (SEQ IDNO: 5) identifying pertinent elements in that sequence.

FIG. 19 provides an exemplary nucleotide sequence for TGFβ1 LAP (cDNA,GenBank Accession NO. BC000125, SEQ ID NO: 6)

FIG. 20 are graphs showing PCA of data in Traumatic Brain InjuryPatients.

FIG. 21 are graphs showing PCA of normalized data in Traumatic BrainInjury Patients.

FIG. 22 is a graph showing IL-6 levels over time for the group of TBIpatients studied.

FIG. 23 is a graph showing TNF levels over time for the group of TBIpatients studied.

FIG. 24 is a graph showing TNF levels over time for the group of TBIpatients studied.

FIG. 25 is a diagram showing relations between variables for TBI used inone embodiment of the ODE modeling described in Example 8.

The use of numerical values in the various ranges specified in thisapplication, unless expressly indicated otherwise, are stated asapproximations as though the minimum and maximum values within thestated ranges are both preceded by the word “about”. In this manner,slight variations above and below the stated ranges can be used toachieve substantially the same results as values within the ranges.Also, unless indicated otherwise, the disclosure of these ranges isintended as a continuous range including every value between the minimumand maximum values.

A “patient” refers to a live subject, such as a human subject or ananimal subject and does not imply a doctor-patient relationship oranimal-veterinarian relationship.

The term “comprising” in reference to a given element of a method,composition, apparatus, etc., means that the method, composition orapparatus includes that element, but also may contain other nonspecifiedelements.

A “vector” is a construct composed of nucleic acids into whichadditional nucleic acids comprising a genetic element are or can beinserted to facilitate transfer of the genetic element into a cell andpermanent or temporary transformation, transfection, expression,incorporation, etc of the cell, typically to either mark the cell, toexpress a genetic element in the cell, or to store, replicate orpropagate the vector and/or genetic element. Vectors containingexpression cassettes are broadly available for expression of genes invarious host cells, such as E. coli, S. cerevisiae, insect and mammaliancells, such as Chinese Hamster Ovary (CHO) cells, human embryonic kidney(HEK) 293 cells, HeLa cells, or other human cells, such as hepatocytes.Although DNA consisting essentially only of a gene for expressing arecombinant protein can be used to transfect or transform a cell, anextremely large number of vector and transformation systems, many ofwhich are well-known and beyond the scope of this disclosure, are usefulin producing a cell that expresses a recombinant protein. Some of thesevector systems are known, including, without limitation: yeast, insect,bacterial, mammalian and viral (for example, phage, retroviral,Adenoviral, and Adeno-associated virus) vector systems. Suitablevectors, cells and, in general, expression systems are availablecommercially from a large variety of sources, including withoutlimitation, Stratagene of La Jolla, Calif. and the American Type CultureCollection (ATCC) of Manassass, Va. In another non-limiting example,plasmid- or episome-based systems useful in gene transfer and expressionare broadly known. Any gene for expression of a give polypeptide orprotein can be inserted into a suitable vector for transfer andexpression in a cell.

By “expression” it is meant the overall flow of information from a gene(without limitation, a functional genetic unit for producing a geneproduct in a cell or other expression system encoded on a nucleic acidand comprising: a transcriptional promoter and other cis-actingelements, such as response elements and/or enhancers; an expressedsequence that typically encodes a protein (open-reading frame or ORF) orfunctional/structural RNA, and a polyadenylation sequence), to produce agene product (typically a protein, optionally post-translationallymodified or a functional/structural RNA). By “expression of genes undertranscriptional control of,” or alternately “subject to control by,” adesignated sequence, it is meant gene expression from a gene containingthe designated sequence operably linked (functionally attached,typically in cis) to the gene. The designated sequence may be all orpart of the transcriptional elements (without limitation, promoters,enhancers and response elements), and may wholly or partially regulateand/or affect transcription of a gene. A “gene for expression of” astated gene product is a gene capable of expressing that stated geneproduct when placed in a suitable environment—that is, for example, whentransformed, transfected, transduced, etc. into a cell, and subjected tosuitable conditions for expression. In the case of a constitutivepromoter “suitable conditions” means that the gene typically need onlybe introduced into a host cell. In the case of an inducible promoter,“suitable conditions” means when an amount of the respective inducer isadministered to the expression system (e.g., cell) effective to causeexpression of the gene. A “chimeric gene” is a gene made by man,typically by recombinant techniques as are broadly known. Nucleic acidsare presented, unless otherwise noted, in a 3′ to 5′ direction. Proteinsand polypeptides are presented, unless otherwise noted, in an N-terminusto C-terminus direction.

Any nucleic acid encoding a given polypeptide sequence or other elementof a gene can be prepared by a variety of known methods. For example andwithout limitation, by direct synthesis of the primary DNA sequence forinsertion in a gene, gene cassette, vector, etc., by PCR cloningmethods, or by restriction and ligation or recombination according towell-established practices. In the case of preparation of a nucleic acidsequence encoding a repetitive sequence, a nucleic acid encoding asingle iteration of the repeat may be prepared with blunt or stickyends, as is known in the art, and subsequently ligated to form multipleiterations. The ligated iterative sequences can then be ligated into avector, gene or gene cassette by known methods.

The term “treatment” and like terms, in the context of the compositions,constructs and devices described herein, refers to the action andability of a peptide to modulate (increase, decrease, reduce, and/orstabilize inflammation, typically associated with a disease orcondition, in a subject, such as a human or veterinary patient. Theability and effective dosage and treatment regimen for a patienttypically is determined by studies of a statistically-relevantpopulation of subjects, and is determined as compared to a placebo orother negative control.

A variety of cell types and cell lines may be used in the bioreactorsand methods described herein. At a minimum, the cells must be able to betransfected (non-viral nucleic acid transfer) or transduced (viralnucleic acid transfer) to permit transfer of appropriate geneticmaterial into the cells. The cells also should have the ability toexpress any genetic construct transferred into the cell in anappropriate manner, such that any genes contained within the transferrednucleic acid material is expressed appropriately—either constitutivelyor in an appropriately regulated manner. The cells should be able tosecrete or otherwise externalize any genetic product of genes containedwithin the transferred nucleic acid and intended to be secreted orotherwise externalized. Lastly, the cells should be capable ofsurviving, if not propagating in any bioreactor. Hepatocyte cell linesor hepatocytes (primary human liver cells) may meet these requirements,as would the common HeLa and HEK293 cells. Hepatocytes may bexenogeneic, allogeneic, isogeneic (syngeneic, when appropriate) orautogenic. Other useful cells or cell lines include: HepG2 hepatocytecell line (American Type Culture Collection HB-8065™), CHO cells, CACO2enterocyte-like cells, A549 lung epithelial-like cells, fibroblast cellsor cell lines, keratinocyte cells or cell lines, or any other cell orcell line that could substitute for a functional or structural cell inany inflammatory disease. The cells may be derived from cell lines,human transplant discards, cell donors, or from the patient's own cellpopulation.

Primary cell cultures or cell lines may be transfected or transducedwith a genetic construct by any useful means, such as by liposome-,electroporation-, particle bombardment- or calcium phosphatemediatedtransfection. Nucleic acids may be transferred into the cell or cellline by transduction, such as by packaging within a suitable transducingparticle, such as an adenovirus (Ad), adeno-associated virus (AAV) orretrovirus (e.g., lentivirus) particle according to any of many knownmethods. In many cases, it is desirable to modify a cell line to includea transferred gene. A number of methods for permanently transforming acell line are known. For example, by flanking a gene with the well-knownretrovirus or AAV terminal repeat structures, or using recombinationsystems, such as the well-characterized CRELOX system, or even by usinglinearized plasmids for random integration, a gene can be introducedinto the genome of a cell line, thereby creating a suitable cell linefor propagation and use in the bioreactors and methods described herein.

A variety of useful bioreactor designs are expected to be useful in themethods described herein. United States Patent Publication Nos.20080145442, 20050049581, 20050032218, 20050015064 and 20050003535, andU.S. Pat. No. 6,759,245, each of which is incorporated herein byreference for its technical disclosure, describe useful examples ofbioreactor devices, how to implement them, useful cell types and relateddevices and methods of use. In the context of the present disclosure, abioreactor is a device containing cells for contact with biologicalfluids of a patient. In its most general sense, a bioreactor comprisesan enclosure which contains the cells, and a membrane which retains thecells within the enclosure, yet permits passage of nutrients andpolypeptides across the membrane.

Extracorporeal bioreactors are cartridges or vessels having at least aperfusion inlet and a perfusion outlet, and a cell compartment, forexample a matrix, within the vessel that provides a suitable environmentfor living cells while allowing perfusion of the cell compartment withsuitable media for maintaining the cells. Such cell compartments can bestructurally build containing semi-permeable membranes, e.g., hollowfiber membranes or flat sheet/plate membranes, with circulation of bloodor plasma on one side of the membrane and the cells on the other side.

As noted above, the bioreactors described herein contain a selectivelypermeable barrier made of a material that allows the passage ofmacromolecules and other cell derived products to and from the subject'splasma or other bodily fluids. The cells themselves do not leave thebioreactor. After circulation and one or multiple passes through thebioreactor, the treated ultrafiltrate (e.g., plasma) may be recombinedwith the cellular components of the subject's blood and returned to thesubject via venous access. When utilizing the bioreactor in a manner inwhich the device is connected to the patient's systemic circulation, thepatient's blood or plasma is supplemented with heparin or otheranticoagulants to prevent clotting. This circulation is maintainedcontinuously for, e.g., a 10 hour support period of extracorporealtherapy. In current similar systems, blood or plasma carries toxins fromthe patient to a bioreactor containing hepatocytes.

One non-limiting embodiment of the present devices include a) abioreactor comprising a fluid treatment compartment and a cellcompartment, and a selectively permeable barrier separating the fluidtreatment compartment and the cell compartment, wherein the cellcompartment comprises a population of cells comprising a gene forexpressing a modulator of an cytokine or other inflammatory agent.

Blood, ultrafiltrate from a subject, or other bodily fluids are passedinto the fluid treatment compartment, where agents secreted by the cellspass into the blood, ultrafiltrate, or other bodily fluids, by passageof the agents across the selectively permeable barrier.

Extracorporeal liver support devices including bioreactors are alsocommonly referred to as bioartificial liver devices (BALDs) orbioartificial liver assist devices (BLADs). A number of such devices areknown in the art and can be adapted for use with MSCs. Exemplarycommercially available extracorporeal liver support device that can beused as described herein include, but are not limited to, the ELAD®system currently marketed by Vital Therapies, Incorporated (shown inFIG. 1 of U.S. Pat. App. Pub. No. 2005/0182349), Circe's HEPATASSIST®,Gerlach's BELS, and Excorp Medical's BLSS. Additional suitable exemplarydevices are described in U.S. Pat. Nos. 6,472,200, 5,605,835; 7,160,719;7,273,465; 6,858,146; 6,582,955; 5,270,192; 6,759,245; and U.S. Pat.App. Pub. No. 20030017142.

In one embodiment, as depicted schematically in FIG. 2, a bioreactor 10comprises a closed vessel 20 that can be of any physical configuration,such as a cylinder, tube, cube, rectangular prism, plastic bag or sacetc. The vessel 20 contains a cell culture 35 composed of cells,suitable media and optionally a cell growth scaffold or matrix. A hollowfiber 30 passes into and out of the vessel 20. For simplicity, only onehollow fiber is depicted, though a typical bioreactor comprises multiplehollow fibers. The hollow fiber is selectively permeable, permittingpassage of gasses and molecules/compounds having a maximum molecularweight (e.g., a maximum of 70, 80, 90, 100, 110, 120, 130, 140, or 150kD, including increments therebetween) or size, preferably so long ascells cannot pass across the barrier. The hollow fiber 30 comprises alumen 35. In use, a fluid, such as cell culture media, and in thecontext of the present disclosure, a patient's bodily fluids, such asplasma, is passed through the lumen 35 of the hollow fiber 30, depictedby arrows A and B. Nutrients and protein constituents of the plasma Ccan pass through the walls of the hollow fiber 30 into the cell culture25. Cytokines, growth factors, immunomodulators and other plasmaconstituents which relate to an inflammatory state also can pass throughthe walls of the hollow fiber 30 into the cell culture 25, to stimulateexpression of, or to inhibit expression of one or more genes containedwithin cells of the cell culture. Factors D produced by cells in thecell culture 25 are able to pass through the walls of the hollow fiber30 and into the plasma or other fluid. In the context of the oneembodiment of the present disclosure, the cells within the vessel aremodified with one or more genes for expression of one or more factorsthat inhibit cytokines or growth factors (which means they in some wayinhibit production of or action of the one or more cytokines or growthfactors).

As would be recognized by those of ordinary skill in the art ofbioreactor design and related fields, this is merely a schematic diagramof one embodiment of the bioreactor. Variations of the number andconfiguration of the hollow fibers, as well as the molecular weightcutoff of the hollow fibers, the types of cells within the device, theirnumber, the presence of one or more opening (closeable, using a valve orother useful closure means) in the vessel for depositing or removingcells, cell media, cell growth scaffolds, drugs, etc. from the vessel,and the size, shape and configuration of the device and its parts arepossible and are a matter of design choice and/or optimization. In oneembodiment, one or more additional hollow fibers are incorporated intothe vessel for use in gas exchange. More specifically oxygen or air canbe passed through the one or more additional hollow fibers to oxygenateliquids within the vessel, and to remove CO₂ from the vessel. “Tubular”does not imply any cross sectional shape of the hollow fiber, only thatthe membrane is a fluid conduit.

Plasma can be separated from cellular components of blood using anultrafiltrate generator or any other plasma filtration method or device.Alternatively, whole blood can be treated by the devices describedherein.

In a second embodiment, illustrated schematically in FIG. 3, thebioreactor device 110 is a transdermal device configured to adhere to apatient's skin 115 in contact with a wound 116. A large variety oftransdermal devices or patches are available and described in theliterature. Unless indicated otherwise, any materials used in therelevant arts for such devices and any device configurations availablein the art are useful for purposes described herein so long as they arenot inconsistent with the functioning of the device as described herein.The device 110 comprises a backing 120 that typically would beocclusive, but which may permit gas exchange. Adhesive 121 is providedto hold the device 110 in place on a patient's skin. A reservoir 126containing cells, cell medium and, optionally a cell scaffolding isprovided and which is sealed between backing 120 and a membrane thatpermits exchange of cytokines, growth factors, immunomodulators andother plasma constituents which relate to an inflammatory state, as wellas nutrients between the wound 116 and the reservoir 125. The membranemay be any suitable membrane.

In yet another exemplary embodiment, depicted schematically for clarityin FIG. 4A, a contact type bioreactor is provided. Bioreactor 210 isprovided, comprising a backing 220 with adhesive about its edges. Thisdiagram is a cross-section viewed from the patient contact side of thedevice. The backing may be any medically or pharmaceutically-acceptablebacking and may be wholly occlusive (preventing passage of gasses andmoisture) or gas-permeable (permitting passage of gasses and moisture).As indicated above, a large variety of occlusive and gas-permeablebackings are available and described in the transdermal device field. Acell growth chamber 225 is shown. A network of hollow fibers 230 isprovided for oxygenation and feeding of cells in the cell growth chamber225. More than one such network may be employed in a device such as thedevice shown. For example a first network of hollow fibers may be usedto supply nutrients in liquid form to the cells, while a second may beused to supply gasses (e.g., oxygen) and to remove CO₂ from the cells.As in all embodiments, the cell growth chamber may comprise a cellgrowth scaffold. In this embodiment, due to the availability of gassesand nutrients via the one or more hollow fiber networks, the backing 220may be wholly occlusive to prevent contamination and to maintainmoisture in the device. The direction of flow of liquid or gassesthrough the network 230 is shown be arrows F. The network 230 comprisesan inlet 231 and an outlet 232, which are attached to a source for theliquid or gasses and a suitable disposal receptacle. Alternately theliquids and/or gasses may be fed to the device 210 and repeatedly passedthrough the device in a closed-loop fashion, provided there is a largeenough reservoir of materials to support growth and/or maintenance ofthe cells in the device over the intended duration of use. Valves 233are shown, which may be used to shut off or restrict flow through theinlet 231 and outlet 232. This may be useful in changing out the device,and in general handling of the device. Connectors (not shown), such asLuer lock or taper fittings may be provided for fluidly connecting inlet231 and outlet (drain) 232 to an external supply/waste disposal orrecirculation system. FIGS. 4B and 4C are cross-sectional schematicdiagrams of alternate embodiments of the device depicted in FIG. 4A,along segment A shown in FIG. 4A. Both of FIGS. 4B and 4C depict backing220, cell growth compartment 225, and hollow fiber matrix 230. Adhesive221 (optional) is shown in these figures attached to lateral extensionsor “tabs” extending from the device. Also depicted is a permeablemembrane 240 for entrapping cells within the compartment 225, which canbe any suitable polymeric or hydrogel composition as are broadly knownand available.

As can be envisioned by one of ordinary skill, the overall structure andcomposition of the devices depicted in FIGS. 4A-C can be variedaccording to design choice and optimization. For instance, adhesive andthe depicted extensions or tabs extending laterally from the device maybe omitted. Likewise, the backing may be omitted, with the barriermembrane extending around and enclosing the cell growth compartment. Forexample, the membrane may form a “bag”, as is depicted in US PatentPublication No. 20050015064 (See, e.g., FIG. 4D). In such an example,the device is wrapped, taped or otherwise placed in contact with awound.

FIG. 4D illustrates the concept of using a perfused flat sheet membranebag in an active wound dressing. The membrane is temporarily placedabove the wound and below the outer wound dressing. Such amembrane-based wound dressing can provide nutrition, oxygenation, pHregulation, electrolyte balance, and detoxification of wound debris.This therapy is expected to improve the clinical outcome by reducing thetime of wound healing while enabling larger treatment areas, and thusreducing the mortality rate in patients with large surface burns. Asshown in FIG. 4D, cells, such as basal keratinocytes, can be applied tothe wound prior to application of the device.

In any of the devices depicted in FIGS. 2, 3 4A-4D, or otherwisedisclosed herein, the devices may be provided with one or more one ormore ports, or openings in the body of the device that are closeable orsealable through which cells, a patient's biofluid or other contentswithin the device may be sampled or cells and/or cell growth scaffolds,or other compounds or compositions may be inserted into the device. Oneor more ports also may be provided for holding or inserting probes foranalyzing the contents of the device, such as temperature probes, pHprobes, oxygen probes or fluorescent light sources and/or fluorescencedetection devices, such as a CCD (Charge-Coupled Device). An opticalport also may be provided for imaging of cells or cell-bearingstructures within the device, for example in combination with afluorescent (excitation) light source.

In the context of burn healing, the inflammatory process associated withburn healing can be modeled by computer and immunomodulatory factorproduction can be controlled to optimize the healing process. TNF,IL-1α, and IL-1β levels are examples of immunomodulatory factors thatmight be controlled in a burn patient to prevent untoward inflammatoryevents. Thus, placing cells transformed with the sTNFR and IL-1ragenetic constructs, is expected to provide control over the inflammatoryprocess. Mathematical modeling or agent-based modeling methods fordetermining targets for modulation/control of the immune response aredescribed, for example, in United States Patent Publication No.20080228456, in a variety of contexts. The choice of cytokines tocontrol, and how strong the control needs to be can be modeled in thismanner. For instance, when a stronger response is necessary to control acytokine such as TNF or IL-1, more cells containing a construct forexpressing an inhibitor of the cytokine may be added to the bioreactor.Cells may be propagated and dispensed into a device either as individualcell populations, or as cells deposited on a cell growth scaffold, suchas beads or ECM sheets. Cells, or cell growth scaffolds comprising cellsmay be stored in any suitable manner that preserves the viability of thecells, such as by freezing or any other suitable manner.

In the devices described herein, the cell-containing compartment maycomprise a cell growth scaffold, such as a collagen, synthetic polymersor decellularized ECM-derived material onto which suitable cells aregrown or maintained. An “ECM-derived material,” is a material preparedfrom an extracellular matrix-containing tissue. Any type ofextracellular matrix tissue can be used in the methods, compositions anddevices as described herein (see generally, U.S. Pat. Nos. 4,902,508;4,956,178; 5,281,422; 5,352,463; 5,372,821; 5,554,389; 5,573,784;5,645,860; 5,771,969; 5,753,267; 5,762,966; 5,866,414; 6,099,567;6,485,723; 6,576,265; 6,579,538; 6,696,270; 6,783,776; 6,793,939;6,849,273; 6,852,339; 6,861,074; 6,887,495; 6,890,562; 6,890,563;6,890,564; and 6,893,666). In certain embodiments, the ECM is isolatedfrom a vertebrate animal, for example and without limitation, from awarm blooded mammalian vertebrate animal including, but not limited to,human, monkey, pig, cow and sheep. The ECM can be derived from any organor tissue, including without limitation, urinary bladder, intestine,liver, esophagus and dermis. In one embodiment, the ECM is isolated froma urinary bladder. The ECM may or may not include the basement membraneportion of the ECM. In certain embodiments, the ECM includes at least aportion of the basement membrane.

Commercially available ECM preparations can also be used in the methods,devices and compositions described herein. In one embodiment, the ECM isderived from small intestinal submucosa or SIS. Commercially availablepreparations include, but are not limited to, Surgisis™, Surgisis-ES™,Stratasis™, and Stratasis-ES™ (Cook Urological Inc.; Indianapolis, Ind.)and GraftPatch™ (Organogenesis Inc.; Canton Mass.). In anotherembodiment, the ECM is derived from dermis. Commercially availablepreparations include, but are not limited to Pelvicol™ (sold asPermacol™ in Europe; Bard, Covington, Ga.), Repliform™ (Microvasive;Boston, Mass.) and Alloderm™ (LifeCell; Branchburg, N.J.). In anotherembodiment, the ECM is derived from urinary bladder. Commerciallyavailable preparations include, but are not limited to UBM (AcellCorporation; Jessup, Md.).

Selectively-permeable membranes useful in the hollow fibers or otherstructures used to transfer include hydrophilic or hydrophobicmembranes, including, without limitation, polypropylene, polyamide,polysulfone, cellulose, or silicon-rubber is preferred for hollow fibermembranes. The selection of hollow fiber membranes depends on themolecules planned for substance exchange. However, any membranes, suchas hollow fiber membranes, useful as substance exchange devices (or massexchange devices), can be used.

Gene constructs for controlling levels of a given factor implicated inthe inflammatory response may be prepared using any of the large numberof recombinant methods described in the literature and which areavailable from companies, such as Invitrogen, Stratagene and Clontech,among many others. Constructs can be assembled from nucleic acidfragments that contain suitable gene elements, such as coding sequences,response elements, etc.

TNF—A TNF construct includes at a minimum a control sequence (promoters,enhancers, response elements, etc.) that increases expression of adownstream (3′) coding sequence in the presence of TNF (not necessarilydirectly responsive to TNF, but also responsive to a cellular eventtriggered by TNF, such as NF-κB) and a sequence encoding an inhibitor ofTNF activity, such as a TNF antagonist or TNF-specific binding reagent,such as a soluble receptor or an antibody or an scFv fragment (cloningand expressing antibody fragments such as an scFV or Fab fragment by,e.g., phage display, is now routinely performed by commercial vendors),or an appropriate “cytokine trap” (see, e.g., Economides, A N et al.Nature Medicine, 9(1):47-52 (2003)).

TNF carries out its inflammatory signaling in cells via activation ofthe nuclear factor-kappa B (NF-κB) pathway and is inhibited by solubleTNF receptor. The classical pathway of NF-κB activation involves aninflammatory response operating through a heterodimer of p50 and p65.NF-κB dimers are held in the inactive state by a family of inhibitorscalled I-κB. Receptor signaling leads to activation of a multisubunitI-κB kinase (IKK) complex which phosphorylates I-κB on two key serines.Phosphorylation of I-κB marks it for degradation by the ubiquitinpathway, so that the NF-κB dimer is liberated to translocate to thenucleus, bind DNA and activate transcription. It is essential that theinflammatory actions of NF-κB are switched off once the inflammatorysignal ceases, and because the inhibitor I-κB is degraded on NF-κBactivation. This means new I-κB must be synthesized. There are threemain members of the I-κB family, two of which, IκBβ and I-κBε aresynthesized constitutively and reestablish NF-κB inhibition on cessationof signaling with a relatively slow time course. Synthesis of the third,I-κBα, is under the control of NF-κB itself, and it is thereforeproduced in response to signaling: it enters the nucleus on synthesis,binds to NF-κB and shuttles it back to the cytoplasm via a nuclearexport signal, switching off NF-κB action with a very short delay, thusmaking NF-κB activity self-limiting.

Accordingly, an appropriate genetic construct, such as a recombinant DNAplasmid containing a gene for expressing soluble TNF receptor (sTNFR),for example an NF-κB-sensitive promoter operably linked (e.g., upstreamof) a sTNFR coding sequence. A soluble TNF receptor is an antagonist ofTNF (see, e.g., US Patent Publication No. 20070249538 for a moredetailed description of sTNFR and variations thereof) and can be derivedfrom TNFR1 (TNFRα or TNFR1a) and TNFR2 (TNFRβ or TNFR1b). An exemplarysequence for STNF1a is provided in FIG. 6B (referenced below). Anexemplary TNF-responsive promoter element is described below and is anNF-κB-sensitive promoter.

IL-1-An IL-1 (for example IL-1β) construct includes at a minimum acontrol sequence that increases expression of a downstream (3′) codingsequence in the presence of IL-1, operably linked to a sequence encodingan inhibitor of IL-1 activity (e.g., by binding IL-1 or otherwisecausing downregulation of either IL-1 production, availability oractivity), such as an IL-1 antagonist or IL-1-specific binding reagent,such as an antibody or an scFv fragment, or an appropriate “cytokinetrap”. An exemplary IL-1-responsive promoter element is described below,and an exemplary IL-1 receptor antagonist (IL-1ra) (also referred to as,IL1rn) sequence is shown in FIG. 16B (referenced below).

TGFβ1—A TGFβ1 construct includes, at a minimum, a control sequence thatincreases expression of a downstream (3′) coding sequence in thepresence of TGFβ1, operably linked to a sequence encoding an inhibitorof TGFβ1 activity (e.g., by binding TGFβ1 or otherwise causingdownregulation of either TGFβ1 production, availability or activity),such as an TGFβ1 antagonist, a TGFβ1 specific binding reagent, such asan antibody or an scFv fragment, an appropriate “cytokine trap” or aLAP. An exemplary TGFβ1-responsive promoter element is plasminogenactivator inhibitor type 1 (PAI1) and an exemplary LAP sequence areshown in FIG. 19 (referenced below).

IL-6-An IL-6 construct includes, at a minimum, a control sequence thatincreases expression of a downstream (3′) coding sequence in thepresence of IL-6. An exemplary control sequence is (SEQ ID NO: 8):

5′-GTATTTCCCAGAAAAGGAACGTATTTCCCAGAAAA GGAACGTATTTCCCAGAAAAGGAAC-3′

This promoter element contains only 3 copies of the relevant responseelement, which can be increased. Of note, this can be activated byvarious ligands including interferon-alpha, interferon-gamma, EGF, PDGFand IL-6. Soluble IL-6 receptor, a “cytokine trap” (see, e.g.,Economides, A N et al. Nature Medicine, 9(1):47-52 (2003)) or a bindingreagent specific to IL-6 may be encoded by this gene (See, generally, SLPlushner, The Annals of Pharmacotherapy, 2008 November, Volume42:1660-68; Economides, A N et al. Nature Medicine, 9(1):47-52 (2003);and Ancey, C, et al. J. Biol. Chem. 278(19):16968-16972 (2003)).

Sepsis

One exemplary therapeutic goal is not to abolish sepsis-inducedinflammation per se but rather to define its time course and reducedamage or dysfunction (i.e. promote healing) by modulating inflammationin a rational fashion. More specifically, our goal is to attenuate thepositive feedback cycle of inflammation damage inflammation, by allowingthe body to re-equilibrate its inflammatory response through a repeated,incremental reduction of pro-inflammatory influences. To do so, we haveconceived of and prototyped a self-regulating device for individualizedcontrol of inflammation.

Sepsis following infection, trauma, or major surgery results inprolonged, expensive intensive care unit hospitalization and remains amajor cause of mortality. It is estimated that over 750,000 patientsdevelop sepsis, of which over 200,000 die. Sepsis is most often causedby bacterial infection, and even more specifically by Gram-negativebacterial infection. The acute inflammatory response to biologicalstress such as Gram-negative bacterial endotoxin (lipopolysaccharide;LPS) involves a cascade of events mediated by a large array of cells andmolecules that locate invading pathogens or damaged tissue, alert andrecruit other cells and molecules, eliminate the offending agents, andfinally restore the body to equilibrium. Inflammation causes damage totissues, which in turn lead to the production of molecules thatre-stimulate inflammation. Perplexingly, this feed-forward loop can leadto persistent, dysregulated inflammation that promotes organ dysfunctionand death.

Our overarching hypothesis is that the acute, self-amplifyinginflammatory response in experimental Gram-negative sepsis is driven inlarge part by cytokines such as TNF and IL-1, and that adaptiveneutralization of these cytokines can result in reduced inflammation,organ damage, and perhaps also improved survival. Our secondaryhypothesis is that computational simulations of the device and diseasestate can streamline the design of this theranostic device and suggestthe optimal protocols for its application.

One embodiment of the prototype inflammation-regulating bioreactor isbased on the production of sTNFR driven by TNF. This design was chosenbecause TNF is the primary driver of a broad array of inflammatorymediators upon stimulation with endotoxin (Brown, K. L., et al. TrendsImmunol. 28, 260-266 (2007)). We propose to create, test, andmathematically model a sepsis theranostic based on a modified liverbioreactor, and in parallel to explore computationally the likelihood ofclinical utility of such a theranostic device. Gene-modified human HepG2cells can act as both diagnostic indicators (of TNF as well as sTNFR)while at the same time modifying the inflammatory response using sTNFR.Development of this device will utilize mechanistic computationalsimulations of the impact of the proposed device on a simulatedpopulation of human septic patients, much as we simulated the responseto neutralizing anti-TNF antibodies in sepsis (Clermont, G. et al. Crit.Care Med. 32, 2061-2070 (2004)) as well as vaccination in the setting ofanthrax (Kumar, R., et al. Shock 29, 104-111 (2008)) The device isenvisioned as being developed using an iterative process of simulationand empirical studies to suggest optimal device characteristics as wellas timing, duration, and extent of neutralization of TNF, IL-1, or otherrelevant inflammatory cytokines; see FIG. 5).

Inflammatory response associated with other disease states orconditions, such as trauma, may be controlled using the methodsdescribed herein. For example TNF and IL-1 are implicated in theinflammatory response associated with trauma, such that control of IL-1and/or TNF should effectively control the inflammatory responseassociated with trauma (See, e.g., United States Patent Publication Nos.20030087285 and 20080228456) discussed above.

EXAMPLE 1 TNF-sTNFR Plasmid

FIG. 6A shows a plasmid map for a TNF-sTNFR plasmid(3×NFkB-sTNFR-pcDNA3). The plasmid comprises three copies of the NFkBresponsive elements with reduced TK promoter driving production ofsTNFR1a. To create this vector we used pcDNA3 (from Invitrogen) as abackbone. Our insert is 3×NFkB-TK+sTNFR1A. The plasmid also comprisesNeoR—gene resistance to Neomycin. This was used for transienttransfection experiments. FIG. 6B provides the sequence of thisconstruct in pertinent part. Stably transfected lines can be produced byspontaneous integration of the vector and can be selected by Neomycinresistance. In response to TNF stimulation, cells will produce sTNFR1A.Response to TNF for this vector in HepG2 cells transiently transfectedwith the plasmid is shown in Example 2.

EXAMPLE 2 Prototype Biofeedback Plasmid

In our prototype biofeedback plasmid, described in Example 1, geneticelements (the NF-κB promoter) responsive to TNF were placed upstream ofthe gene coding for mouse sTNFR (FIG. 6A6B). TNF carries out itsinflammatory signaling in cells via activation of the nuclearfactor-kappa B (NFκB) pathway and is inhibited by sTNFR. Accordingly, wecreated a recombinant DNA plasmid containing the mouse NF-κB-sensitivepromoter upstream of the mouse sTNFR gene, and inserted this plasmidinto human cells that we felt would be appropriate for long-term,high-level expression of this recombinant gene product (the HepG2 livercell line).

The reason for the choice of mouse soluble TNF receptor and human cellline was that we could stimulate the cells with a mouse cytokine andobtain the species-specific cytokine inhibitor, while hopefully avoidingthe confounding result that would occur if we were to detect the sTNFRproduced by the HepG2 cells themselves.

To make the plasmid, we obtained plasmids that contained each elementseparately (a NF-kB response element was obtained by PCR from a plasmid(3×NFkBTK109) containing that sequence, and sTNFR sequence was obtainedfrom the sTNFR ImageClone™ [Invitrogen] plasmid), as well as a plasmidthat allows for high-levels gene transcription in mammalian cells(pcDNA3). Next, the TNFdriven sTNFR plasmid was inserted into HepG2cells, which were stimulated with mouse TNF following by assay of mousesTNFR (FIG. 7). Control studies included assaying human sTNFR (todetermine if there is any contribution from the human HepG2 cells' ownsTNFR), no stimulus, and various other controls (FIG. 7). As seen inthis figure, we have created a circuit in which 1) no sTNFR is producedfrom HepG2 cells transfected with the negative control plasmid or theTNF-driven sTNFR plasmid in the absence of TNF (FIG. 7A), and 2) TNF isproduced significantly above background from HepG2 cells transfectedwith the TNF-driven sTNFR plasmid following stimulation with TNF (FIG.7A), and 3) that only the TNF-driven sTNFR construct led to asignificant reduction in the levels of TNF. We extended these studies toassess the dose-responsiveness of our construct to mouse TNF (FIGS. 7and 8). Our results suggest that maximal activation was approximately3.5-fold relative to baseline (FIG. 8). We note that this may be anunder-estimate, since the assay may not recognize sTNFR bound to TNF andsince constructs using the same TNF-responsive element upstream ofluciferase rather than sTNFR suggested a simulation of up to 12-fold(data not shown). We have also successfully transfected HepG2 cells withour proposed fluorescent protein (FIGS. 11A-11B).

In another experiment, HepG2 cells transfected with this plasmid wereplaced in a bioreactor and tested for their initial response to TNF aswell as for the time for this initial response to decay. 27×10⁶ HepG2cells were transfected with the 3×NFkB-sTNFR-pcDNA3 vector and wereseeded in an 8 ml bioreactor. Samples were collected at a rate of 1tube/per hour. The cells were stimulated with TNF as follows: 5 day 0ng/ml TNF; 1 day 3 ng/ml TNF; 1 day 0 ng/ml TNF; 1 day 1 ng/ml TNF; 1day 0 ng/ml TNF. Results are shown in FIG. 9.

We also carried out studies on establishing the culture conditions forHepG2 cells, in both standard 2-D and in bioreactor cultures. Weutilized a four-compartment, hollow fiber culture bioreactor in whichcells can spontaneously reassemble to tissue-like structures in a 3-Dperfused cell compartment. (Gerlach, J. C. Bioreactors forextracorporeal liver support. Cell Transplant. 15 Suppl 1, 591103(2006)) Importantly, the bioreactor comes in several distinctconfigurations and volumes, including 8 mL, 2 mL, and 1 mL. Importantly,the 1 mL bioreactor is optimized for imaging, a design that mayfacilitate optical detection of fluorescent or other tagged proteinsused for determination of either the patient's own local or systemicinflammatory state or of the production of relevant proteins by thebioreactor in response a given patient's inflammatory response.

The prototype inflammation-regulating bioreactor was created as follows.The 3-D nature of the cell compartment allows cells to spontaneouslyform tissue-like structures (Gerlach, J. C. et al. Improved hepatocytein vitro maintenance in a culture model with woven multicompartmentcapillary systems: electron microscopy studies. Hepatology 22, 546-552(1995) and Zeilinger, K. et al. Time course of primary liver cellreorganization in three-dimensional high-density bioreactors forextracorporeal liver support: an immunohistochemical and ultrastructuralstudy. Tissue Eng 10, 1113-1124 (2004)), similar to those found in vivo,and the convection-based mass transfer as well as the mass exchange inthe cell compartment allow restructuring of neo-sinusoidalendothelialized perfusion channels. In turn, these channels allow forphysiologic perfusion and flow/pressure alterations as in parenchymalorgans. Within 2-3 days of culture, liver cells spontaneously formtissue-like structures, including neo-sinusoids, (Gerlach, J. C. et al.Improved hepatocyte in vitro maintenance in a culture model with wovenmulticompartment capillary systems: electron microscopy studies.Hepatology 22, 546-552 (1995)) with neo-formations of spaces of Dissélined by endothelial cells and structures resembling the Canals ofHering, the anatomical stem cell niche of liver progenitor cells. Thevascular-like perfusion allows for long-term support of a cell massunder substantial high-density conditions.

Each bioreactor contains two bundles of hydrophilic polyether sulfonehollow fiber microfiltration membranes (mPES, Membrana, Wuppertal,Germany) for transport of culture medium (forming 2 independent mediumcompartments), interwoven with one bundle of multilaminate hydrophobichollow fiber oxygenation membranes (MHF, Mitsubishi, Tokyo, Japan) fortransport of oxygen and carbon dioxide (forming a gas compartment). Thefibers are potted within a polyurethane housing (Gerlach, J.,Schauwecker, H. H., Hennig, E., & Bucherl, E. S. Endothelial cellseeding on different polyurethanes. Artif. Organs 13, 144-147 (1989))(PUR, Morton, Bremen, Germany), and cells are inoculated through 24silicone rubber tubes (Silastic, Dow Corning, N.Y., USA). Cells are thuscultured in the interstitial spaces between the fibers (the fourthcompartment, the cell compartment). The microfiltration fibers (Gerlach,J., Stoll, P., Schnoy, N., & Neuhaus, P. Comparison of hollow fibremembranes for hepatocyte immobilisation in bioreactors. Int. J. Art.Org. 19, 610-616 (1996)) have a molecular weight cut off of MW 400 kDa,allowing larger proteins to pass freely through the fiber walls and intothe cell compartment. Culture medium circulates from the lumens of themicrofiltration fibers to the cell compartment and back to the fiberlumens, due to the axial pressure drop from the inlet to the outlet ofeach fiber lumen (Starling flow)(Starling, E. H. On the absorption offluid from the convective tissue space. J. Physiol 19, 312-326 (1896);Kelsey, L. J., Pillarella, M. R., & Zyndney, A. L. Theoretical analysisof convective flow profiles in a hollow-fiber membrane bioreactor.Chemical Engineering Science 45, 3211-3220 (1990); and Bruining, W. J. Ageneral description of flows and pressures in hollow fiber membranemodules. Chemical Engineering Science 44, 1441-1447 (1989)). Medium ispumped through the two-microfiltration fiber bundles in opposingdirections (counter-current flow), allowing the medium entering the cellcompartment from one bundle (at its high pressure end) to exit byreentering the same bundle (at its low pressure end) or by entering theother bundle (at its low pressure end, adjacent to the first bundle'shigh pressure end). This complex flow pattern mimics an “arterial andvenous” flow in natural tissues ensures that the medium in the cellcompartment is well-mixed, so that most of the cells are exposed to thesame low concentrations of nutrients, toxins, and waste products, as inthe natural liver sinusoids. Additionally, the interwoven oxygenationfibers (Gerlach, J., Kloppel, K., Stoll, P., Vienken, J., & Muller, C.Gas supply across membranes in bioreactors for hepatocyte culture.Artif. Organs 14, 328333 (1990)) ensure that most of the cells receiveadequate oxygen delivery and carbon dioxide removal. The gas flowthrough the oxygenation fibers can be considered as laminar,fully-developed flow of a compressible Newtonian fluid in a circulartube, allowing an analytical solution predicting the gas flow rate as afunction of the axial pressure drop along the fibers (Federspiel, W. J.,Williams, J. L., & Hattler, B. G. Gas flow dynamics in hollow-fibermembranes. Aiche J. 42, 2094-2099 (1996)).

The bioreactor is integrated into a processor-controlled perfusiondevice with electronic pressure and flow regulation. Modular pump unitsfor recirculation and fresh media feed, respectively, with exchangeablemulti-channel flow heads and gears serve for medium recirculationand—substitution to provide constant levels of pH and nutrition to thecells. A heating unit provides a constant temperature within theperfusion circuit. Flow rates of compressed air and carbon dioxide (CO₂)are controlled by 2 rotameters with a gas-mixing unit. The perfusiontubing with bubble traps is made of standard medical grade dialysis PVC(B. Braun, Melsungen, Germany). Sterilization is performed with ethyleneoxide at 60° C. according to clinical standards. We describe in thisExample the first study using this type of bioreactor seeded with thegene-modified HepG2 cells (transfected with the TNF-driven sTNFR DNAconstruct).

The 3-D nature of the cell compartment of the prototype bioreactorallows cells to spontaneously form tissue-like structures, (Gerlach, J.C. Bioreactors for extracorporeal liver support. Cell Transplant. 15Suppl 1, S91-103 (2006)). similar to those found in vivo, and theconvection-based mass transfer as well as the mass exchange in the cellcompartment allow restructuring of neo-sinusoidal endothelializedperfusion channels. In turn, these channels allow for physiologicperfusion and flow/pressure alterations as in parenchymal organs. Inthese bioreactors, hepatocytes and HepG2 cells form neo-sinusoids,endothelialized spaces of Dissé, and Canals of Hering. (Gerlach, J. C.Bioreactors for extracorporeal liver support. Cell Transplant. 15 Suppl1, S91-103 (2006)). Each bioreactor contains two bundles of hydrophilicpolyether sulfone hollow fiber microfiltration membranes (mPES,Membrana, Wuppertal, Germany) for transport of culture medium (forming 2independent medium compartments), interwoven with one bundle ofmultilaminate hydrophobic hollow fiber oxygenation membranes (MHF,Mitsubishi, Tokyo, Japan) for transport of oxygen and carbon dioxide(forming a gas compartment). The fibers are potted within a polyurethanehousing (PUR, Morton, Bremen, Germany), and cells are inoculated through24 silicone rubber tubes (Silastic, Dow Corning, N.Y., USA). Themicrofiltration fibers have a molecular weight cut off of MW 400 kDa,allowing larger proteins to pass freely through the fiber walls and intothe cell compartment. Culture medium circulates from the lumens of themicrofiltration fibers to the cell compartment and back to the fiberlumens, due to the axial pressure drop from the inlet to the outlet ofeach fiber lumen (Starling flow) (Gerlach, J. C. Bioreactors forextracorporeal liver support. Cell Transplant. 15 Suppl 1, S91-103(2006)), with a complex flow pattern that mimics an “arterial andvenous” flow found in natural tissues. The interwoven oxygenation fibersensure adequate oxygenation and carbon dioxide removal via 2 rotameterswith a gas-mixing unit. The bioreactor is integrated into aprocessor-controlled perfusion device with electronic pressure and flowregulation. A heating unit provides a constant temperature within theperfusion circuit. The perfusion tubing with bubble traps is made ofstandard medical grade dialysis PVC (B. Braun, Melsungen, Germany).

FIG. 10 shows that we can grow HepG2 cells both in standard 2-D (FIG.10A) and bioreactor cultures (FIGS. 10B-C), and that cells cultured thuscan respond to mouse TNF-α by producing mouse sTNFR above those levelsdriven by the basal promoter (FIG. 10D). We expect to improve upon thedegree of inducibility of sTNFR and increase the duration of theexperiment by creating stably transfected HepG2 cells. Moreover, weutilized an Analytical Bioreactor, which is optimized for imaging tocarry out fluorescence studies (FIG. 11C).

Induction of fluorescent protein expression by cytokine/grow factor(e.g. TNF levels) can be used for monitoring these factors in patient,as a means of assessing the local or general inflammatory state of thepatient.

Designing a “theranostic” variant of the biohybrid device—We envisionthis device as a true “theranostic,” meaning that we wish to not onlymodify the course of acute inflammation but also to track in nearreal-time. We therefore propose to assess the amount of active TNF aswell as sTNFR both directly (by Luminex™ and ELISA assays) andindirectly, by triggering the production of a fluorescent protein inaddition to sTNFR in response to TNF. We will pursue to complementarystrategies to achieve this goal. In the first, HepG2 cells will betransfected with a construct consisting of the TNF-sensitivepromoter/enhancer element (see, e.g., Example 1) upstream of afluorescent protein in order to detect TNF indirectly. Based on theliterature regarding existing fluorescent proteins, most availablefluorescent proteins have maturation times longer than 8 hours (a delaythat is too long to be useful for diagnostic purposes). One protein,mCherry (Clontech), has a maturation time of 15 min and another,TurboFP635 (Evrogen, Inc.; FIGS. 11A and 11B) has a 24 min maturationtime. We chose to proceed with the latter since it produces a largerquantum yield (0.34) versus 0.22 for mCherry. This approach will allowus to assess the relative levels of TNF, which can be confirmed andcalibrated against a TNF-α assay (e.g. Luminex™ or ELISA).

In the second approach, we will place a fluorescent protein downstreamof the sTNFR coding region in a manner that will allow the production ofthe fluorescent protein only if sTNFR is produced. To do so, we havebegun to construct a vector that includes an Internal Ribosomal EntrySite (IRES)²⁶ and a fluorescent protein. IRESs are relatively short DNAsequences that can initiate RNA translation in a 5′ cap-independentfashion. Placement of the IRES and a second gene of interest (ORF 2)downstream of the first target gene (ORF 1) allows co-expression of ORF1 in a cap-dependent manner and ORF 2 in a cap-independent fashion, thusfacilitating translation of two proteins from one mRNA transcript.²⁶ Forcreation of constructs with bicistronic expression of TurboFP635, wehave created a vector which contains IRES followed by TurboFP635 (datanot shown), which will simplify subsequent work on future bicistronicvectors. This second approach will allow us to assess the degree ofsTNFR production indirectly. Similarly to our proposed strategy for thedetection of TNF (see above), we will compare the results of thefluorescence studies to the results of a mouse-specific sTNFR ELISA(FIGS. 7, 8, and 9).

The above studies will be carried out using HepG2 cells transientlytransfected with the various constructs in standard 2-D culture in orderto establish optimal experimental conditions. We will examine theproduction of TurboFP635 in response to various doses of TNF (0.1, 0.3,1, 3, and 10 ng/mL) at various time points (0, 1, 2, 4, 8, and 24 h). Wewill then progress to studies in bioreactor culture, in which we willduplicate the dose-curve and time course studies based on the data fromthe 2-D culture experiments. We will make use of the 1 mL, 4-chamberAnalytic Bioreactor. This bioreactor provides four separate cellchambers with approximately 120 μL of volume in each chamber for cells.A separate inoculation port is provided for each chamber. Each of thechambers is connected to the fiber pathways to expose all the chambersto common media recirculation. Thermonox cover slips on the bottom ofthe chambers, and transparent lids for light transmission, allow realtime optical microscopy of the cells in the cell chambers. TheAnalytical Bioreactor can remain connected to the full bioreactor setup,including the heating element that ensures that the cells will receiveculture medium at 37° C., while fluorescence imaging is performed (ZeissAxiskop 40 and JenOptik cooled CCD camera). Fluorescence can bequantified using JenOptik, Optimas, NIH Image, Scion Image, or similarsoftware.

In parallel, we will carry out studies using a bioreactor setup using a2-mL bioreactor, in which we will repeat variants of the experimentdescribed in FIG. 9. In this experiment, we will vary the dose of mouseTNF injected into the bioreactor (0.1, 0.3, 1, 3, and 10 ng/mL), theflow rate of medium through the bioreactor, and the length of the mediumwashing period. We will determine immunofluorescence at various timepoints (0, 1, 2, 4, 8, 24, 48, 72, and 96 h). If our simulations suggestthat we need to ramp up sTNFR production more rapidly than our currentconstruct allows in order to achieve an optimal outcome in vivo, we willmodify our TNF-responsive element (e.g. by adding or removing NF-κBelements). We will then create a stable transfection vector as follows.The TNF-responsive element (NF-κB) and sTNFR open reading frame from ourNF-kB-sTNFR-pcDNA3 (Example 1) will be inserted into the pLenti6.3vector (Invitrogen). The packaging cell line 293FT will be transfectedwith the new vector together with a packaging vector mixture(ViraPower™, Invitrogen). Stably transfected HepG2 cells will beproduced by infection with viral stock following antibiotic selectionand follow the procedure described in that figure. These cells will thenbe seeded into a liver bioreactor (see below).

Our proposed device would serve to “ratchet down” the positive feedbackinflammatory loops set in motion by endotoxin or any otherTNF-α-inducing stimulus and self-augmented proximally in large part byTNF-α itself (Jones, A. L. et al. Cancer Surv. 8, 817-836 (1989)).Accordingly, have characterized the production of TNF-α and its naturalinhibitor (sTNFR) in endotoxemic mice and rats. In mice subjected to 3mg/kg endotoxin, TNF-α reached a peak by 90 min and declined rapidly(Chow, C. C. et al. Shock 24, 74-84 (2005)), while sTNFR rose by 30 min,remained elevated until ˜12 h and then declined slowly (FIG. 12). Wehave observed similar dynamics of TNF-α in rats subjected to 3 mg/kg LPS(FIG. 13A) (Daun, S. et al. J. Theor. Biol. 253, 843-853 (2008)). Mostof these data have served as the basis for calibration of mathematicalmodels of the acute inflammatory response in mice (Chow, C. C. et al.The acute inflammatory response in diverse shock states. Shock 24, 74-84(2005)) and rats (FIG. 13A) (Daun, S. et al. J. Theor. Biol. 253,843-853 (2008)), respectively (though the rat E. coli peritonitis dataare as yet unpublished). Importantly, we have carried out detailedstudies in rats subjected to sepsis induced by the intraperitonealimplantation of a fibrin clot containing various inocula of E. coli(FIGS. 13B & C). From these studies, we have learned that the peak ofTNF production as well as bacterial counts in survivable sepsis in thisexperimental model occurs at approximately 48 h (FIGS. 13B & C). Thus,the fact that our current generation of engineered HepG2 cells producessTNFR with a lag of approximately 8-12 h and reaches a peak atapproximately 24-48 (FIGS. 8 and 9) suggests that we have a realistictime frame for the inhibition of sTNFR if our goal is to allow TNF todrive bacterial killing while minimizing the tissue damaging, laterauto-induction of TNF has the potential for success.

The design and refinement of the inflammation-regulating bioreactorfollows an iterative, cyclic process (FIG. 5). We collect data oninflammatory analytes, markers of organ damage, and outcomes in thepresence or absence of the device. We then carry out Principal ComponentAnalysis in order to define both the “internal” variables and the“external” inputs and outputs to the model, which are the variables thatcan or could potentially be controlled by the theranostic device. Weidentify and modify the parameters that govern the model. Alternatively,the literature may be searched directly in order to extract informationneeded for the generation of mathematical models of inflammation in agiven disease.

We describe this process in greater detail. We generate a large datasetof inflammatory analytes (which we call a vector) from the varioussamples taken in the rat at a specific time or from the bioreactor invitro. We will utilize statistical analysis and data-driven modeling(predominantly using Principal Component Analysis, probit and logitmodels, and our recently-developed process of Dynamic Profiling [seebelow]) to derive information about the primary drivers of inflammationin the presence or absence of bioreactor-based intervention. The dataobtained from this complementary approach will serve to 1) point us tonovel components of inflammation modified by the neutralization ofTNF-α; 2) help us to define parameter values for our mechanistic models;and 3) help us construct reduced mechanistic models that will be moreamenable to formal analysis (as we have done in the past (Vodovotz, Y.et al. Mechanistic simulations of inflammation: Current state and futureprospects. Math. Biosci. 217, 1-10 (2009)). Importantly, we will comparethe predictions from statistical models with the predictions of themechanistic models.

Standard statistical analyses (t-test, ANOVA, etc., as appropriate) ofthese data will be extended to the creation of data-driven models aswell as our newly developed Dynamic Profiling method (see below). Thestatistical models would attempt to inter-relate data obtained in thecourse of Aims 2 and 3 by way of extracting principal components of theoutput vector of cytokine readings vs. relevant responses (cell death ordifferentiation, production of glucose or lactate by cells in thebioreactor, etc). Principal components are linear combinations of theoutput vector (normalized so as to have Euclidean length 1), with theproperty that they carry the largest variance in several orthogonaldirections. This is a dimensionality reduction tool that allows one tomonitor significant variation in the output of our devices, byconcentrating on just a few (usually up to five or six) statisticallymost significant orthonormal linear combinations. These combinations arecalled the leading principal components. They would be our signatureresponses, and we will model them as a time series of correlatedresponses (within a patient from the various assays, and betweenpatients as repeated measures on each patient). Repeated measurementsdesigns, MANOVA techniques and multivariate ARIMA models with anon-diagonal covariance structure are the primary statistical toolsexpected to be used. Another method we would utilize involves morestandard regression modeling. Though we cannot, strictly speaking,derive direct mechanistic insights from such modeling, this analysiswill help us in understanding the factors that drive the temporalevolution of the pre-eminent responses, as well as highlighting thecentral drivers of these responses.

In parallel, we will carry out our mechanistic (mathematical) modelingstudies. We will modify our existing models to account for 1) abacterial pathogen, similar to several of our earlier mathematicalmodels of inflammation (Clermont, G. et al. In silico design of clinicaltrials: a method coming of age. Crit. Care Med. 32, 2061-2070 (2004);Kumar, R., Clermont, G., Vodovotz, Y., & Chow, C. C. The dynamics ofacute inflammation. J. Theoretical Biol. 230, 145-155 (2004); andReynolds, A. et al. A reduced mathematical model of the acuteinflammatory response: I. Derivation of model and analysis ofantiinflammation. J. Theor. Biol. 242, 220-236 (2006)); 2) the effect ofconnecting just the bioreactor itself to the rat's circulation in thepresence or absence of bacterial infection; 2) the effect of modulatingflow rate and other parameters of the bioreactor; and finally 3) theeffect of the full bioreactor that will produce sTNFR in response toTNF. We have previously studied in detail dose- and time-varyingproduction of various cytokines, including TNF, in rats subjected tobacterial endotoxin or E. coli fibrin peritonitis (FIG. 13).

Data such as these, as well as published studies on modeling the removalof inflammatory mediators (for example (Clermont, G. et al. In silicodesign of clinical trials: a method coming of age. Crit. Care Med. 32,2061-2070 (2004); Kumar, R., Chow, C. C., Bartels, J., Clermont, G., &Vodovotz, Y. A mathematical simulation of the inflammatory response toanthrax infection. Shock 29, 104-111 (2008); and Waniewski, J. &Prikrylova, D. A mathematical model of extracorporeal antibody removalin autoimmune disease. Int. J. Artif. Organs 12, 471-476 (1989)) andmany others) will be used as the starting point for our simulationstudies. We will start simulating the characteristics of theinflammationregulating bioreactor by modeling the basic function of thebioreactor as shown in the equations below. These simulations wouldprogress to include data obtained on flow rates in the bioreactor,clearance rates of TNF and sTNFR, and the data derived on the relativefluorescence with respect to actual TNF and sTNFR production. Inparallel, we will model the fluorescence data that will act as proxiesfor the production of TNF and sTNFR, using methods published by others(Wang, X., Errede, B., & Elston, T. C. Mathematical analysis andquantification of fluorescent proteins as transcriptional reporters.Biophys. J 94, 2017-2026 (2008)). This work will include estimation ofthe production and maturation of TurboFP635.

$\begin{matrix}{\frac{\left\lbrack {{TNF}\; \alpha} \right\rbrack}{t} = {{{Flow\_ rate} \cdot C_{{TNF}\; \alpha}} - {{r_{1}\lbrack{sTNFR}\rbrack}\left\lbrack {{TNF}\; \alpha} \right\rbrack} - {d_{1}\left\lbrack {{TNF}\; \alpha} \right\rbrack}}} \\{\frac{\left\lbrack {{NF}\; \kappa \; B} \right\rbrack}{t} = {{K\left( \left\lbrack {{TNF}\; \alpha} \right\rbrack \right)} - {d_{2}\left\lbrack {{NF}\; \kappa \; B} \right\rbrack}}} \\{\frac{\lbrack{sTNFR}\rbrack}{t} = {{G\left( \left\lbrack {{NF}\; \kappa \; B} \right\rbrack \right)} - {{r_{1}\lbrack{sTNFR}\rbrack}\left\lbrack {{TNF}\; \alpha} \right\rbrack} - {d_{3}\lbrack{sTNFR}\rbrack}}} \\{\frac{\lbrack C\rbrack}{t} = {{{r_{1}\lbrack{sTNFR}\rbrack}\left\lbrack {{TNF}\; \alpha} \right\rbrack} - {r_{2}\lbrack C\rbrack}}}\end{matrix}$

Using these data, as well as data on markers of organ damage/dysfunctionin the animals, we will calibrate our mathematical model to experimentaldata using data-fitting algorithms that we have already deployed (Chow,C. C. et al. The acute inflammatory response in diverse shock states.Shock 24, 74-84 (2005); Wang, X., Errede, B., & Elston, T. C.Mathematical analysis and quantification of fluorescent proteins astranscriptional reporters. Biophys. J 94, 2017-2026 (2008); and Torres,A. et al. Mathematical modeling of post-hemorrhage inflammation in mice:Studies using a novel, computer-controlled, closedloop hemorrhageapparatus. Shock 32, 172-178 (2009)). Given a textual specification ofthe equations, and values for the coefficients and initial conditions,the integrator writes out files of time series data for analytes in themodel. We will collect analyte data, which will be aggregated to producethe most likely value across the population (for example, we may takethe mean, median, or some more complex statistical analysis of thedata). Thus, we will have created a new, general time series for eachanalyte. To fit the model to the data, we will begin with a set oftentative coefficient values, which may be random, or may be seeded withinitial values that take advantage of some expert knowledge of thesystem. In each iteration, a new set of candidate values will begenerated, the resulting model will be evaluated, and the predictionerror of the model will be computed with respect to the aggregated timeseries. This error information will be then direct the choice ofparameter values in the ensuing iteration. This process will be repeateduntil our searches have converged upon a point whose error cannot beimproved, thus producing the best-fit model for the population.

EXAMPLE 3 TNF-sTNFR Plasmid

FIG. 14A shows a plasmid map for plasmid TNF-sTNFR(pLenti6-3×NFkB-sTNFR-IresTurboFP). The plasmid contains three copies ofthe NF-kB responsive elements with a reduced thymidine kinase (TK)promoter driving a coding sequence for sTNFR1a (soluble TNF-α receptor).Other features include: IRES (Internal Ribosomal Entry site, whichallows production of a second protein from same mRNA; TurboFP635(modified red fluorescent protein, with a short maturation time) andBlasticidineR (gene conferring resistance to the antibiotic compoundBlasticidine; stably transfected lines can be selected by Blasticidineresistance). In response to TNF stimulation, cells will produce sTNFR1Aand TurboFP635. Of note, genes carried on lentiviral vectors, such aspLenti6.3, can be integrated by lentiviral transduction methods as areknown in the art.

All vectors were sequenced with using BigDye3.1 sequencing kit onABI3100 or ABI3730 sequencer. FIG. 14B provides confirmatory sequencesfor pertinent portions of plasmid TNF-sTNFR.

Other NF-κB-responsive promoters were tested, as shown in FIG. 15. Itshould be recognized that different promoters can yield differentresults, depending on the cell type, though in most cases choice ofpromoter would be a matter of optimization for a given diseaseapplication based on the statistical and mathematical modeling analysisdescribed above.

EXAMPLE 4 IL-1-IL-1ra Plasmid

FIG. 16A shows a plasmid map for plasmid IL-1-IL-1ra(IL1RE-IL1ra-IresTurboFP-lenti6.3). The plasmid comprises three copiesof the IL-10 responsive elements with reduced TK promoter drivingproduction of IL-1ra (soluble IL-1 receptor). Other features include:IRES (Internal Ribosomal Entry site, which allows production of a secondprotein from same mRNA; TurboFP635 (modified red fluorescent protein);and BlasticidineR (gene conferring resistance to the antibiotic compoundBlasticidine; stably transfected lines can be selected by Blasticidineresistance). In response to IL-1 stimulation, cells will produce IL-1raand TurboFP635. FIG. 16B provides confirmatory sequences for pertinentportions of plasmid IL-1-IL-1ra.

For the backbone of this construct, we used pLenti6.3N5DESTverA_R1R2(from Invitrogen). Our insert is 3×IL1RE-TK-IL1rn-IRES-TurboFP635. IRESis derived from the Clonetech pIRES vector. It contains a GC-rich regionwhich cannot be sequenced, and therefore might contain differences fromthe depicted sequence. In an additional experiment, we switched fromIRES to IRES2, which is much stronger and should produce increasedamounts of fluorescent protein. We also can use a “self-cleaving”peptide sequence (like T2A, P2A, etc., see, e.g., Szymczak et al.,Correction of multi-gene deficiency in vivo using a single‘self-cleaving’ 2A peptide-based retroviral vector. Nat. Biotechnol.2004 May; 22(5):589-94. Epub 2004 April 4) for co-production therapeuticprotein and detection protein.

Although we are currently using TurboFP635 as a fluorescent tag orindicator, other fluorescent/luminescent proteins should be equallyuseful in this context. That said, we believe that TurboFP635 and TagGFP(both from “Evrogen”) are preferred because they have very shortmaturation time (12-24 min) as compare with most other proteins. Foroptimal detection, fast-maturing and degrading proteins are mostdesirable.

EXAMPLE 5 Alternative TNF-sTNFR Plasmid

FIG. 17A shows a plasmid map for an alternative TNF-sTNFR plasmid(pLenti6-3×NFkBsTNFR-T2A-TurboFP). The plasmid comprises three copies ofan NF-κB responsive elements with reduced TK promoter driving productionof sTNFR1a (soluble TNF receptor). Other features include: T2A(“self-cleaving” peptide); TurboFP635; and BlasticidineR (geneconferring resistance to the antibiotic compound Blasticidine; stablytransfected lines can be selected by Blasticidine resistance). Inresponse to stimulation with TNF-α, cells will produce sTNFR1A andTurboFP635 proteins. FIG. 17B provides confirmatory sequences forpertinent portions of plasmid pLenti6-3×NFkB-sTNFR-T2ATurboFP.

EXAMPLE 6 TNF-TurboFP Plasmid

FIG. 18A shows a plasmid map for a TNF-TurboFP plasmid(pLENTI6-3×NFkB-TurboFP). The plasmid comprises three copies of theNF-κB responsive elements with reduced TK promoter driving production ofTurboFP635. The plasmid also contains BlasticidineR, so that stablytransfected lines can be selected by Blasticidine resistance. Inresponse to stimulation with TNF-α, cells will produce TurboFP635protein. This vector can be used as a diagnostic for TNF-α (and byinference, possibly also for the general or local inflammatory state ofthe patient) by fluorescence. FIG. 18B provides confirmatory sequencesfor pertinent portions of plasmid pLENTI6-3×NFkB-TurboFP.

EXAMPLE 7 LAP Expression Vector

A plasmid can be produced operably linking the TGFbeta responsiveelement (from the PAI-1 promoter)5′-TCGAGAGCCAGACAAAAAGCCAGACATTTAGCCAGACAC-3′ (SEQ ID NO: 7). 12 copiesof this sequence before minimal adenovirus MLP promoter can givestimulation 1300 folds in HepG2 cells (see, Dennler, S. et al. The EMBOJournal Vol. 17 No. 11 pp. 3091-3100, 1998; see also GenBank AccessionNo. NM_(—)000660 for the structure of TGFbetal and FIG. 19.

EXAMPLE 8 Modeling of Traumatic Brain Injury

Below, we describe various aspects of work designed to yield amathematical model of the inflammatory response in the setting oftraumatic brain injury (TBI). Such a mathematical model is envisioned asserving for the prediction of the injury outcome and for selecting anefficient treatment protocol (including a treatment using aspecifically-tailored inflammation-regulating bioreactor describedextensively above). Such a computational model could relate the cytokinedata of patients with the patients' health or local or overall extent oftissue damage.

In our work, we utilized cytokine data for several patients with TBI.There are several obstacles for constructing good mathematical modelfrom the given data set. One concern is that the dimension of the dataset is quite large (13) so we cannot directly associate data with modelvariables since the resulting model would be too complicated. Weaddressed this issue by performing statistical analysis on the data. Weused principal component analysis (PCA) to reduce the dimension of data,and also we carried out correlation analysis to further reduce redundantvariables. A limitation is that we do not have data representing suchimportant components of the immune response as the concentration of theinflammatory cells and measure of damage. Without these components it ismore difficult to calibrate our prediction of inflammatory cellsconcentration and damage accurately, and this may lead to several modelswith the same fitting result for the cytokine data but with differentbehavior of predicted damage. We are going to overcome this difficultyby constructing the ensemble of models (Daun et al., 2008).

We obtained cytokine data for 33 patients. The data is given for thefollowing cytokines: IL-113, IL-2, IL-4, IL-5, IL-6, IL-8, TNF-α, IL-13,MIP-1α, MIP-1β, VEGF, IL-1α, and IL-10. Therefore, the dimension ofcytokine data is 13. Besides cytokine data we also have some patientrelated data such as patient age, gender, etc. For each patient data isgiven at discrete time points. The average number of time points for allpatients is 13.

To reduce the dimension of cytokine data we first carried out aprincipal component analysis and correlation analysis (PCA) for thisdata. Both analyses were performed on all cytokine data at once withoutconsideration of different time points and distinct patients. The mainreason for such procedure was that we don't have much data for eachpatient and the number of patients is relatively small. For everystatistical analysis (and for PCA especially) it is best to have as manydata points as possible to obtain the best results. So it was better touse a mixed data rather than to use small amount of data. We obtainedthe results shown in FIG. 20 from PCA of the original cytokine data.

It is seen from FIG. 20 that IL-6 and IL-8 played the main role in thefirst principal component. But these results do not give muchinformation about data because the principal role of those cytokines canbe easily explained by the fact that their values change in the mostsignificant way from almost 0 to more than 1000; meanwhile, othercytokines do not change to the same extent. Hence, the variances of IL-6and IL-8 are very high and they contribute to the principal componentsmore than others. To address this issue we normalized the data bydividing each column of cytokine data by its norm. After this weperformed PCA again and obtained the results shown in FIG. 21

Now it is seen that the first principal component is a linearcombination of TNF-α, MIP-1α, MIP1β, IL-8, IL-10, IL-6. It is aplausible result since in many mathematical models of inflammationresponse the cytokines from this list are used.

To reduce the dimension of cytokine data even more we also looked at thecorrelation matrix of cytokines. There were quite high correlationcoefficients between MIP-1α and MIP-1β, as well as between IL-8 andIL-6. Since MIP-1α, MIP-1β, and IL-8 are chemokines we decided to useonly one variable describing chemokine instead of three variables.Therefore we are left with only three cytokines: pro-inflammatorycytokines TNF-α, IL-6, and anti-inflammatory cytokine IL-10. Throughoutthe following analysis, only TNF-α, IL-6, and IL-10 were analyzed.

Cytokine data for each patient (33) were reviewed (data not shown). Fromthe visual examination of graphs showing cytokine data over time, IL-6demonstrated the most interesting behavior. For most of the patients theinitial value of IL-6 was high and it rapidly decayed to low value. ThenIL-6 has a peak near t=40, or near t=70, or it has both these peaks. Atthe end of the observation, IL-6 begins to decay. TNF-α also has aprominent peak for some patients. Peaks of IL-10 occur a moment laterafter peaks of IL-6 and TNF-α, which confirms the anti-inflammatorynature of IL-10: when inflammation progresses (the level of IL-6 andTNF-α are high), then more IL-10 is produced, and as result theinflammatory response decays.

To better understand the relation between time and peaks of cytokines wecarried out the following analysis. First of all, we unified the timescale for all patients. In order to achieve this we divided the timeinterval [0, 120] into 20 intervals of equal length, and consideredlinear interpolation of the cytokine data for each patient. Then wechose the middle point of each interval and took the average value ofeach cytokine at each interval. After this procedure we got cytokinedata for all patients at the same time points. If the data for anypatient at the particular interval was missing then we assigned specialvalue for such an interval. The next step was to analyze thedistribution of peaks of cytokines in time. We decided not to simplycompare the value of cytokines at the given point with some thresholdvalue to understand whether the value of cytokine is high or low, butinstead we computed the local variance for each cytokine (local in thesense that we took only 3-4 points for computing the variance) and thenwe compared this value with a threshold value. If at the given point thelocal variance was high then we assigned 1 to this point (interval),otherwise we assigned 0. After processing all patients we got threematrices of 0's and 1's (the rows of matrices corresponded to thepatients, and the columns to the time) which represented the behavior ofcytokines in time: one (1) stands for significant changes of values(i.e. a peak), and zero (0) tells that there is no significant changes.We then summed up the numbers of ones at each column and divided thesevalues by the number of patients for which data was available for thegiven interval. We plotted the resulting vector and obtained thedistribution of local variance (peaks in some sense) for cytokines intime.

FIG. 22 confirms that IL-6 indeed has peaks at t=40 and t=70 for manypatients, and also that its initial value is high and decaysimmediately.

From FIG. 23, for TNF-α it is not very easy to draw any usefulconclusions. It seems that the peaks of TNF-α can occur at any time.This supports the conclusion that result that TNF-α plays a significantrole in the PCA.

FIG. 24 for IL-10 partially resembles the figure for IL-6. But theinitial value of IL-10 is low and increases.

We constructed several ordinary differential equation) mathematicalmodels for the inflammatory response that accompanies TBI. Herein, only2 models are presented. As indicated above, a limitation here is that wehave data only for cytokines, and no data for damage or inflammatorycells. Cytokines are produced by inflammatory cells, which in turn aredirectly activated by cytokines and indirectly activated by tissuedamage. All models have the same number of variables and equations:damage D, inflammatory cells M, chemokine C, TNF-α, IL-10, and IL-6.Relations between these variables are presented in FIG. 25.

FIG. 25 is a diagram showing relations between variables for TBI used inthe ordinary differential equation modeling described below. Solid linesrepresent production and dashed lines represent inhibition.

The first model is described by the following equations:

$\begin{matrix}{{\frac{D}{t} = {{d_{0}M} - {d_{1}D}}},} \\{{\frac{M}{t} = {\frac{m_{0}D}{1 + {m_{1}D}} + \frac{m_{2}C}{1 + {m_{3}C}} - {m_{4}\left( {M - 1} \right)}}},} \\{{\frac{C}{t} = {\frac{c_{0}D}{1 + {c_{1}D}} - {c_{2}C}}},} \\{{\frac{{IL}_{10}}{t} = {{i_{0}M} - {i_{1}{IL}_{10}}}},} \\{{\frac{{TNF}}{t} = {\frac{t_{0}M}{1 + {t_{1}{IL}_{10}}} - {t_{2}{TNF}}}},} \\{\frac{{IL}_{6}}{t} = {\frac{b_{0}M^{6}}{1 + {b_{1}{IL}_{10}}} - {b_{2}{{IL}_{6}.}}}}\end{matrix}$

We used the simplest possible equation for the damage. The equationdescribing the time evolution of inflammatory cells (M) plays a centralrole in this mathematical model, because the level of these cellsregulates the level of all cytokines in this model. The decaying termfor M is of the form (M−1) because we wanted to put a lower bound forMat the level 1 in order to take advantage of high powers of M: if thelevel of M were less than 1, then the high powers of M would result in aslow growth rate (while our intention is in fact to use high powers torepresent high growth rate). We used the sixth power of M in theequation describing the time evolution of IL-6 because our experimentswith lower powers gave poorer results when attempting to fit to thecytokine data from TBI patients (data not shown). Actually, the highpower of M in the equation for IL-6 can be explained from the behaviorof IL6, namely that this cytokine changes rapidly from low values tohigh values. This case is used to illustrate a specific example; otherpowers may also be used in order to fit the mathematical model topatient data. The obvious problem with this model is that there is nofeedback (positive or negative) from the equations for cytokines to thefirst three equations. Nevertheless, the fitting results, even for thissimple model, were good.

Our next model is the following:

$\begin{matrix}{{\frac{D}{t} = {{d_{0}M} - {d_{1}D}}},} \\{{\frac{M}{t} = {{\left( {\frac{m_{0}D}{1 + {m_{1}D}} + \frac{m_{2}C}{1 + {m_{3}C}} + \frac{M_{4}{TNF}}{1 + {m_{5}{TNF}}} + \frac{m_{6}{IL}_{6}}{1 + {m_{7}{IL}_{6}}}} \right)\frac{1}{1 + {m_{8}{IL}_{10}}}} - {m_{9}M}}},} \\{{\frac{C}{t} = {\frac{c_{0}D}{1 + {c_{1}D}} - {c_{2}C}}},} \\{{\frac{{IL}_{10}}{t} = {{i_{0}M} - {i_{1}{IL}_{10}}}},} \\{{\frac{{TNF}}{t} = {\frac{t_{0}M}{1 + {t_{1}{IL}_{10}}} - {t_{2}{TNF}}}},} \\{\frac{{IL}_{6}}{t} = {\frac{b_{0}M^{6}}{1 + {b_{1}{IL}_{10}}} - {b_{2}{{IL}_{6}.}}}}\end{matrix}$

Here we changed only the equation for inflammatory cells. Thismathematical model incorporates the positive feedback frompro-inflammatory cytokines IL-6 and TNF, and negative feedback from theanti-inflammatory cytokine IL-10. This model showed better fittingresults than the first model.

Before fitting our models to the actual TBI patient data, we modifiedthe data values themselves. We multiplied the cytokine data byappropriate coefficients to get approximately the same scale for allcytokines. We did this in order to have the same boundaries forparameters in our model. For each model we have three more parameters:the initial values of tissue or overall damage, inflammatory cells, andchemokine. We wrote a MatLab code for performing fitting procedure forour models. At this time, we fit each model for individual patientsonly.

Our fitting procedure is the following. We choose one patient, then wechoose the initial guess for parameters (randomly or based on previousparameter estimations), and we use the Nelder-Mead simplex method forparameter optimization. The error function in our case is the sum oferrors between computed values of cytokines from our equations and thevalues from original data set (we compute the Euclidean distance betweentwo data vectors). Other methods for parameter fitting, either publishedor proprietary, may also be used for this purpose.

The results for several patients were obtained for both models, or forthe second model only.

As a subsequent step, an analysis of the parameter set thus obtainedwould be performed to understand the relations between and among theparameters.

In future work, we will continue improving our model, though we we wantto keep the model quite simple, and not consider large complicatedmodels with hundreds of parameters. Next we plan to find a way to dividepatients who have the similar cytokine behavior into groups, and thentry to fit all patients in one group using the same set of parameters(only initial values of damage, M, and C may be different for distinctpatients). Also, we intend to construct an ensemble of models to betterpredict the behavior of the damage just from one model with fixedparameters.

We claim:
 1. A method of modulating wound healing, sepsis, trauma, ortraumatic brain injury (TBI), comprising, providing bodily fluid of apatient to an extracorporeal bioreactor, wherein the extracorporealbioreactor comprises: a compartment comprising cells and a selectivelypermeable membrane in contact with the cells that does not permitpassage of the cells and which permits passage of a first cytokine inthe bodily fluid of the patient, the cells comprising a chimeric genecomprising a response element operably linked to a sequence encoding asecond cytokine or a cytokine inhibitor, in which the response elementcauses expression of the second cytokine or cytokine inhibitor when thecells are contacted with the first cytokine in the bodily fluid of thepatient; contacting the bodily fluid with the selectively permeablemembrane, such that the first cytokine in the bodily fluid can passthrough the selectively permeable membrane and the second cytokine orcytokine inhibitor produced by the cells can pass into the bodily fluid;and returning the bodily fluid to the patient.
 2. The method of claim 1,in which the cells are selected by use of a computer model of aninflammatory response characteristic of a disease or condition in thepatient.
 3. The method of claim 1, comprising modeling inflammationassociated with sepsis and determining one or more cytokines to inhibitor produce to control inflammation in the patient associated withsepsis.
 4. The method of claim 3, comprising determining levels of oneor more cytokines in the patient and modeling inflammation using the oneor more levels of cytokines in the patient and determining a cytokinelevel to be controlled in the patient to determine a chimeric geneconstruct to place in the bioreactor based on an outcome of themodeling.
 5. The method of claim 4, comprising modeling inflammationassociated with TBI and determining one or more cytokines to inhibit orproduce to control inflammation in the patient associated with TBI. 6.The method of claim 5, comprising determining levels of one or morecytokines in the patient and modeling inflammation using the one or morelevels of cytokines in the patient and determining a cytokine level tobe controlled in the patient to determine a chimeric gene construct toplace in the bioreactor based on an outcome of the modeling.
 7. Themethod of claim 1, in which the patient is a TBI patient.
 8. The methodof claim 7, in which one or both of an inhibitor of TNF and an inhibitorof IL-6 are produced by the cells.
 9. The method of claim 1, in whichthe chimeric gene expresses a cytokine inhibitor selected from the groupconsisting of soluble TNF receptor, IL-1 receptor agonist, and TGF-β1LAP (latency-associated peptide).
 10. The method of claim 1, in whichthe cells comprise one or more genes that express an inhibitor of one orboth of TNF and IL-1α or IL-1β.
 11. The method of claim 1, in which thecells comprise one or more genes that express one or both of soluble TNFreceptor and IL-1 receptor agonist.
 12. The method of claim 1, in whichthe compartment comprising the cells comprises a plurality ofselectively permeable hollow fibers passing through the compartment inwhich the plurality of hollow fibers are fluidly connected to a plasmaor blood circulation system in which blood or plasma from the patient iscirculated through the hollow fibers and into the patient.
 13. Themethod of claim 1, in which the compartment comprising the cells has atleast one wall that is the selectively permeable membrane, in which theselectively permeable membrane is placed in contact with a wound on thepatient or a bodily fluid in situ in the patient.
 14. The method ofclaim 13, in which the compartment comprising the cells comprises aplurality of selectively permeable hollow fibers passing through thecompartment through which one or both of a gas and a fluid comprisingnutrients for the cells is passed.
 15. The method of claim 1, in whichthe cells are hepatic cells.